JP2022524083A - Systems and methods for drug-independent patient-specific dosing regimens - Google Patents

Abstract

The systems and methods described herein use a computerized medication regimen recommendation system to determine a patient-specific medication regimen for a patient. The systems and methods herein are configured such that a computerized drug medication regimen recommendation system evaluates and recommends one or more patient-specific medication regimens that apply not only to a single drug but also to certain classes of drugs. As such, we provide a drug-independent model. Drug-independent models offer superior utility to drug-specific models by accessing extensive clinical data collected for all of the drugs in the drug category. Rather than implementing multiple models, each model corresponding to a single drug within a category, a drug-independent model is applicable to all drugs within that category and therefore a range of administrations. It can be applied to a wider range of patients across the pathway than a single drug model.

Description

(Mutual reference of related applications)
The present application is U.S. Practical Application No. 16 / 391,950, filed April 23, 2019, and March 8, 2019, the contents of which are incorporated herein by reference in their entirety. 35 U.S. with International Application No. PCT / US2019 / 028750 and US Provisional Application No. 62 / 815,825 filed on the same day. S. C. Claim the benefit of priority under Section 119 (e) (US Patent Act Article 119 (e)).

(Technical field)
The present disclosure generally, but is not limited to, for drug-specific mathematical models and treatments specific to certain classes of drugs in order to predict, propose, modify, and evaluate suitable drug treatment regimens for specific patients. Regarding patient-specific medications and treatment recommendations, including computerized systems and methods that use the observed patient-specific responses.

(background)
The physician's decision to initiate a drug-based treatment regimen for a patient involves determining a dosing regimen for the drug to be prescribed. Different dosing regimens are appropriate for different patients with different patient factors. As an example, dosing quantity, dosing interval, treatment duration, and other variables can vary across different dosing regimens. For example, a patient with a low neutrophil count may require a delayed dose, a lower dose than a typical patient, or both. Traditionally, physicians prescribe an initial dosing regimen based on the drug package insert (PI) and the physician's own personal clinical experience. After the initial cycle of treatment, the physician may follow up the patient, reassess the patient, and reconsider the initial dosing regimen. The PI sometimes provides quantitative indications for increasing or decreasing the dosage, or increasing or decreasing the dosing interval. Based on both the patient's physician's assessment, its clinical experience, and PI information, the physician will flexibly adjust the dosing regimen. Adjusting the dosing regimen for a patient is largely a trial-and-error process that is informed by the experience and clinical judgment of the physician.

Appropriate dosing regimens can be very beneficial and therapeutic, while improper dosing regimens can be ineffective or even harmful to the patient's health. In addition, both underdose and overdose generally result in loss of time, money, and / or other resources, increasing the risk of unwanted outcomes. A computerized dosing recommendation system can assist healthcare professionals in providing and assessing dosing regimens. Previously disclosed examples of the system and method were filed October 7, 2013 and issued September 25, 2018, US Pat. No. 10,083,400, and April 8, 2016. US Pat. Incorporated in the book).

Several known population pharmacokinetic models have been developed to account for a particular drug and are specific not only to the drug, but also to the specific patient population taking the drug. Such models are often, as well as specific, specific to a particular route of administration. These models are used with the drug and route of administration so that the model can be used to simulate other dosing regimens and compare the pharmacokinetics and various other applications of the drug after the formulation has been modified. It is intended to be as specific as possible with respect to the patient, both of whom are expected to take the drug during the course of treatment of the specific disease. This approach, which develops a model for a single drug, limits its application to that drug, as well as the specific patient population for which data was collected to create the model. Limiting model development and usage to a single drug limits the amount of information and data available.

(wrap up)
Thus, a system and method for determining a patient-specific medication regimen for a patient using a computerized medication regimen recommendation system that applies to one category or other group of drugs is described herein. Explained in the book. The systems and methods described herein provide a population model that can be used with respect to certain classes or sets of drugs. In particular, the systems and methods herein are designed to develop patient-specific dosing regimens based on data showing pharmacokinetic (“PK”) and pharmacodynamic (“PD”) parameters of multiple drugs. Can be configured in. Models for certain groups of drugs are physiological, for example, from different drugs and / or routes of administration, because the model is specific drug independent and utilizes patient-specific data based on similarity between drugs. It will be possible to predict patient response and optimize future medications based on previous measurements of the parameters. Information about the administered drug is also acquired by comparing the measured data with model-based simulations of other drugs in the group that can then be investigated against the results so far. As such, it may also be desirable for the model to be applicable to a set of agents and not dependent on the specific agent. Models applicable to a group of drugs are tailored to the physician, who may wish to treat patients with multiple drugs or to vary certain therapeutic factors such as the route of administration. obtain.

The dosing regimen (also referred to as a treatment plan) may include a schedule for dosing, one or more dosages, and / or one or more routes of administration. The dosing regimen is not limited to just one drug and can include multiple drugs using the same or different routes of administration. A drug (also referred to as a drug, drug, drug, biologic, compound, treatment, therapy, or any other similar term) is a substance that has a physiological effect when introduced into the body. In some implementations, the systems described herein are not specific to a particular drug, but instead apply to certain classes, subaggregates, or groups of drugs used in drug-independent models. Will be done. As used herein, the term "drug" may refer to a single drug or a class or set of drugs.

Certain categories of drugs exhibit at least one similar pharmacokinetic (PK) and / or pharmacodynamic (PD) behavior, share a common mechanism of action, or a combination thereof, more than one. It can be a group of drugs. As an example, a set of drugs may be used to treat the same disease or for the same indication, the example of which is systemic inflammatory bowel disease, inflammatory bowel disease (IBD), ulcer. Includes sexual colonitis, Crohn's disease, rheumatoid arthritis, tonic spondylitis, psoriasis arthritis, psoriasis, asthma, or multiple sclerosis. A set of drugs may have a similar chemical structure. For example, the set of drugs may include monoclonal antibodies (mAbs), anti-inflammatory compounds, corticosteroids, immunomodulators, antibiotics, or biological therapies. Users, such as doctors, clinicians, or users who create drug-independent models, may define drug categories based on specific criteria, and the components of that category are in the database. It may be designated electronically as a part. The database is accessible to the systems and methods disclosed herein for use in determining a category-based dosing regimen that may be used for any drug within the category. In some implementations, the dosing regimen output from the model is not specific to a single drug, but is general to the drug category and is suitable for any drug in that category. For example, a dosing regimen may include measurements of drug-independent units (eg, one unit, two units, three units, etc., where the units correspond to a defined amount of activator) and the time or time for administration. It may include multiple times.

The development and application of drug-independent models allows for different usefulness than a single drug-specific model. Rather than implementing multiple models, each model corresponding to a single drug within a category, a drug-independent model is applicable to all drugs within that category, and therefore to a certain extent. It can be applied to a wider range of patients than a single drug model across routes of administration. For example, the models described herein may include pharmacokinetic drug-independent models that can be used for all biologics used in the treatment of inflammatory diseases. good. Such models use the same model to include fully human monoclonal antibodies (mAbs), chimeric mAbs, fusion proteins, and mAb fragments (ie, with different pharmacokinetic properties, but with different molecular weights and adaptations, etc. Can be used to suggest a dosing regimen for a range of drugs) with similarities. The model may be used in a broad patient population, including inflammatory bowel disease, rheumatoid arthritis, psoriatic arthritis, psoriasis, multiple sclerosis, and other such diseases resulting from immunodysregulation. can. The development and application of drug-independent Bayesian models for agents in other broad drug sets (eg, aminoglycoside antibiotics, chemotherapeutic agents that cause low white blood cell counts, etc.) is similarly feasible.

Drugs within the category may be administered via various routes such as subcutaneous, intravenous, or oral. The drug-independent model may consider the route of administration by taking it as a variable input to the system and allowing greater flexibility for the model. Because the drug-independent model applies to a set of drugs, not just a single drug, the model provides patient-specific information when a patient is treated with multiple drugs in a set of drugs. May be reserved. For example, the set of drugs may include infliximab, vedolizumab, adalimumab, and other anti-inflammatory biologics. If the patient is treated with one drug (eg, infliximab) and then later with another drug (eg, vedrizumab), the model is once the patient is treated with a new drug. And, when determining the appropriate dosing regimen, all patient-specific data (drug concentration measurements, clearance rates, weight measurements, etc.) from the patient's infliximab-based treatment may be reserved. Reserving patient-specific data allows the drug-independent model to accurately predict the patient's ability to process the drug, thereby making more patient-specific medications when the patient changes drug therapy. Allows you to provide a regimen.

One aspect of the invention relates to a method for generating a patient-specific drug dosing regimen. As discussed in detail below, the method can be a computer system, including a single computer or multiple computers communicating over any network, such as in a distributed architecture. It may be implemented in. The at least one processor may be housed in one, some, or all of the computers in the computer system and may be stored on the same or different computers in the computer system, at least one. It may communicate with one electronic database. The system may include a cloud-based computing system operated by the same, related or unrelated entities.

The method comprises receiving an input in the processor of the system. The input is drug data indicating multiple drugs and associated routes of administration, wherein the drug within the multiple drugs has similar pharmacokinetic (PK) behavior, similar pharmacodynamic (PD) behavior, or similar pharmacodynamic (PD) behavior. It may contain drug data that are expected to present both. For example, a drug may have a similar chemical structure or mechanism of action that leads to similar pharmacokinetic (PK) behavior, similar pharmacodynamic (PD) behavior, or both. The input may include concentration (for PK model) or response data (for PD model) indicating the specific drug concentration or response level of the multiple drugs in the sample obtained from the patient. The input may include a target drug exposure or response level for the patient. Targets may be determined by the physician based on drug data and / or concentration or response. In some implementations, the target may be automatically determined by the system to provide a therapeutic response in the patient. The system may evaluate multiple targets entered to determine one or more targets that result in a therapeutic response in the patient.

The method includes selecting a mathematical model from a database stored in the memory of the system. The database may be accessible by the processor and may store multiple mathematical models. The mathematical model selected may represent the response of multiple patients to one or more of the multiple drugs. Each response of a response indicates a patient response to at least one drug among a plurality of drugs. In some cases, the mathematical model is not specific to a particular drug. In other cases, the model is first specific to a particular drug and then adapted over time using the methods described herein, even if applied to certain classes of similar drugs. good. In other cases, the user can use a drug-specific model to predict concentrations or responses to other drugs, and a drug-specific model can be used for drug-independent predictions. Can be found.

The method comprises inferring multiple predicted concentration-time profiles using the selected mathematical model and based on the inputs. Each predicted concentration time profile may indicate the patient's response to any drug among multiple drugs via a particular route of administration. Each predicted concentration time profile of the plurality of predicted concentration time profiles may correspond to the dosing regimen in the plurality of dosing regimens output by the system. Each dosing regimen may include at least one dosage and / or a recommended schedule for administering at least one dosage to the patient. The method may include selecting a first dosing regimen for a plurality of drugs from a plurality of dosing regimens in order to achieve a therapeutic objective based on the target drug exposure or response level.

The method may include outputting a first dosing regimen for a plurality of drugs for the patient. The advantage of at least one of the methods is that the model is one drug in a set of drugs, eg, to provide better recommendations for administering any of the drugs in the set of drugs. The model has access to a wider range of clinical data compared to the drug-specific model, as it can be refined to use the measured data from the administration of.

In some implementations, the mathematical model is selected based on one or more criteria such as input, errors associated with the model, or commonality between each model and multiple drugs. In some implementations, the mathematical model is selected based on drug data. If the model is selected based on drug data, the selection step may involve comparing the model's parameters or covariates with the drug data. This step may involve maximizing the number of similar parameters between the model and the set or category of drug. Each model in the database may be associated with an error or confidence interval that indicates the model's previous performance, and the model may be selected based on the error or confidence interval. The model may be selected based on the amount of data or information available or applicable to the model. The selected model uses the widest range of information or data available so that the model can be refined to make more accurate predictions and provide recommendations that are more likely to reach the target. That would be advantageous. For example, the concentration or response data and / or the type of concentration or response data can be analyzed and the type of data incorporated into the model, or at least some or the maximum of the data. You may choose a model that can be incorporated. In some implementations, the selection is carried out by the optimization function. Instead of, or in addition to, the selection method discussed above, the Bayesian method may be used to select the model. In some implementations, multiple models may be compared against each other and the model that best represents the patient or input may be selected.

In some implementations, drug data excludes information that identifies specific drugs belonging to multiple drugs. The drug data may include one or more available dosage units corresponding to one or more drugs among the plurality of drugs. In such cases, the dosage may be configured to be a multiple of the available dosage unit. In some implementations, the method further comprises receiving additional drug data indicating an updated route of administration for a specific drug or one or more drugs among multiple drugs. The system may update a mathematical model based on an updated route of administration for a specific drug among multiple drugs or for one or more drugs. The updated mathematical model can then be used to calculate at least one updated dosing regimen to reach therapeutic objectives for the patient. At least one updated dosing regimen may be output for the patient.

In some implementations, the model is a pharmacokinetic model. The model may be a pharmacodynamic model as an alternative. The model can explain both pharmacokinetics and pharmacodynamics. The pharmacokinetic or pharmacodynamic components of the model can indicate concentration-time profiles of multiple drugs. The pharmacokinetic component of the model may be based on clearance parameters that represent the inflow and outflow of the drug into the patient's body. The pharmacodynamic component of the model may be based on the synthesis and degradation rate of pharmacodynamic markers that indicate the individual response of the patient to multiple drugs. In some implementations, the model comprises a pharmacokinetic component and a pharmacodynamic component, the components are interrelated. For example, the clearance of the pharmacokinetic component is a function of the pharmacodynamic response, and / or vice versa. The model may employ Bayesian methods such as Bayesian inference to predict patient response or concentration-time profiles for one or more dosing regimens.

In some implementations, the method further provides additional patient data showing the patient's second response to specific drug administration according to the first dosing regimen or a modified version of the first dosing regimen. Includes steps to receive. Additional patient data may include additional concentration data indicating one or more concentration levels of a particular drug in one or more samples obtained from the patient. The model can then be updated based on the patient's second response to specific drug administration according to the first dosing regimen or a modified version of the first dosing regimen. At least one updated dosing regimen can be calculated to reach therapeutic objectives for the patient using the updated mathematical model. At least one updated dosing regimen can be output for the patient.

The route of administration is at least one of subcutaneous, intravenous, oral, intramuscular, intrathecal, sublingual, intraoral, rectal, vaginal, intraocular, nasal, inhalation, spray, skin, or transdermal. May be. The multiple drugs may be one of monoclonal antibodies and / or antibody constructs, cytokines, drugs used for enzyme replacement therapy, aminoglycoside antibiotics, and chemotherapeutic agents that cause leukocyte depletion. In some implementations, each drug in multiple drugs shares a similar chemical structure. Each drug among multiple drugs may share a similar mechanism of action. In some implementations, multiple drugs treat inflammatory diseases such as inflammatory bowel disease (IBD), rheumatoid arthritis, ankylosing spondylitis, psoriatic arthritis, psoriasis, asthma, and multiple sclerosis. Used for. The specific drug may be infliximab or adalimumab. The drug data may include a dosing intensity for the particular drug and / or an indicator indicating whether the particular drug is entirely human-derived, chimeric, or fragmented. As input, the system may receive input patient data indicating the patient's disease to be treated.

In some implementations, the received input contains physiological data indicating one or more measurements of at least one physiological parameter of the patient. At least one physiological parameter of the patient is an inflammation marker, albumin measurement, drug clearance indicator, C-reactive protein (CRP) measurement, anti-drug antibody measurement, hematocrit level, biomarker of drug activity, Weight, body size, gender, race, stage, disease status, previous therapy, previous clinical test results information, concomitant medications, complications, Mayo score, partial Mayo score, Harvey Bloodshaw index , Blood pressure reading, psoriasis area, severity index (PASI) score, disease activity score (DAS), sharpness score, and at least one of demographic information. The mathematical model may be selected from a set of mathematical models to best fit the received physiological data.

In some implementations, the method, for display, is of a patient-specific concentration-time profile and concentration data and additional concentration data showing the patient's response to multiple drugs in response to the first dosing regimen. Includes steps to generate at least some of the indications. Target drug exposure or response level indications may occur for display. In some implementations, the system receives historical data showing the patient's response to a second drug other than the specific drug. Computational models may consider historical data to generate predictions of concentration-time profiles of multiple drugs in a patient. At least one advantage provided herein is to generate a prediction of a patient's response to a class of drugs, including drugs similar to one or more previously administered drugs. It relates to a model that can be applied to a wider range of data than typical, such as data collected from one or more patients administered.

Another aspect relates to a method for generating a patient-specific medication regimen using a computerized medication regimen recommendation system without having previously measured concentration data. The method may be implemented on any of the systems described herein. The method involves receiving an input into the processor of the system. The input may be drug data indicating multiple drugs and routes of administration. A drug among a plurality of drugs may be expected to exhibit similar pharmacokinetic behavioral effects, similar pharmacodynamic effects, or both. The input may include initial drug concentration input data representing the initial comparative concentration data points. The target drug exposure or response level is included in the input. The method further comprises selecting a mathematical model that represents the response of multiple patients to one or more of the drugs. Each response may indicate a patient response to at least one of the plurality of drugs. The mathematical model may be constructed so that it is not specific to a particular drug.

The method comprises using the selected model and input to infer multiple predicted concentration-time profiles that indicate the patient's response to any drug among multiple drugs across the route of administration. Each predicted concentration time profile may correspond to a dosing regimen within a plurality of dosing regimens. Each dosing regimen may include at least one dosage and / or a recommended schedule for administering at least one dosage to the patient. The method comprises selecting a first dosing regimen for a plurality of presumed drugs to achieve a therapeutic objective based on the target drug exposure or response level. The first dosing regimen may be output for the patient. At least one advantage of this aspect is a predicted concentration-time profile configured to meet therapeutic objectives using only initial comparative concentration data points without having measured concentration data from a specific patient. And the resulting dosing regimen from the model.

In some implementations, the initial comparative concentration data points indicate the specific drug concentration level of multiple drugs in the sample obtained from the patient. Alternatively, the initial comparative concentration data points are calculated based on the patient's physiological parameters so that no direct concentration measurements from the patient are required. Patient physiological parameters include inflammation markers, albumin measurements, drug clearance indicators, C-reactive protein (CRP) measurements, anti-drug antibody measurements, hematocrit levels, drug activity biomarkers, body weight, and body. Size, gender, race, stage, disease status, previous therapy, previous clinical test result information, concomitant medications, complications, Mayo score, partial Mayo score, Harvey Bloodshaw index, blood pressure reading It may include at least one of value, psoriasis area, severity index (PASI) score, disease activity score (DAS), sharpness score, and demographic information. The drug data provides an indicator indicating the available dosing intensity and / or whether the specific drug is entirely human-derived, humanized, chimeric, or fragmented with respect to the specific drug. It may be included.

Another aspect relates to a system comprising a controller configured to carry out the method of the aforementioned aspect. The system may include an interface connection to an electronic medical record (EMR) database. The interface may be operable by software or firmware configured to read one or more indicators of patient information for the patient via an interface connection. Appropriate medications for administration to the patient may be determined using the patient information retrieved. This aspect is where the physician can enter the patient's name and the EMR about the patient will automatically feed drug and / or physiological data into the system to make initial guesses. In terms of deafness, it offers at least one advantage. This aspect specifically allows new drugs to be used by drug-independent models by simply adjusting for new drug specificities such as dosing intensity to generate dosing recommendations. It may be useful when recommending a dosing regimen for a relatively new drug because it may have structural similarities and similar identified covariates with other drugs. For example, the new drug may have only clinical data from the FDA review. The clinical trial data can be fed into a drug-independent model system and normalized to other drugs within a similar drug category or group. Without much knowledge of new drugs, especially those used to dosing on a case-by-case basis, physicians will use the systems and methods proposed herein and are based on historical data of similar drugs. Can find the appropriate dosage for the new drug.

Another aspect relates to pharmaceutical preparations, including a first dosage of a specific drug. The first dosage may be determined by the method described herein. For example, a first dosage of a drug in a formulation has an inferred concentration-time profile for multiple drugs, including a specific drug, and then based on the entered target exposure or response level of the drug. Determined by the system to meet the therapeutic objectives. Each drug of the specific drug and the plurality of drugs may have similar pharmacokinetic, pharmacodynamic, and / or structural properties. The formulation may include a modified first dosage determined by the system using an updated mathematical model, the updated mathematical model being one or more of a plurality of drugs. Consider concentrations or response data that indicate the patient's response to the dosage of the drug.

The aforementioned and other purposes and advantages, together with the accompanying drawings, become apparent as a result of consideration of the embodiments for carrying out the invention below, and similar reference symbols are used throughout the specification in a similar manner. Point to.

FIG. 1 is a flow chart illustrating a method for determining a patient-specific dosing regimen for a set of drugs, with an exemplary implementation.

FIG. 2 is a block diagram of a system for implementing the methods described herein, with an exemplary implementation.

FIGS. 3A and 3B are simulated to show the expected patient response to multiple drugs for a given dosing regimen to meet target levels based on a drug-independent model, according to an exemplary implementation. It is an exemplary graph depicting a simulated concentration / time profile.

FIG. 4 illustrates the process for developing a drug-independent model for a set of drugs with an exemplary implementation.

FIG. 5 is a flow chart illustrating a process for modifying an adaptive dosing system with an exemplary implementation.

FIG. 6 shows a system diagram of a computer network for an adaptive dosing system with an exemplary implementation.

7A and 7B are exemplary displays of the user interface on the clinical portal that provide some recommended dosing regimens with an exemplary implementation. 7A and 7B are exemplary displays of the user interface on the clinical portal that provide some recommended dosing regimens with an exemplary implementation.

(Detailed explanation)
The systems and methods described herein use a computerized drug medication regimen recommendation system that applies to one category or other group of drugs to determine a patient-specific medication regimen for a patient. .. The systems and methods described herein provide a population model that can be used for certain classes or sets of drugs. In particular, the systems and methods herein are designed to develop patient-specific dosing regimens based on data indicating pharmacokinetic (“PK”) and pharmacodynamic (“PD”) parameters of multiple drugs. Can be configured. Examples of such drugs are included in Table 1. The following definitions are used in Table 1, where "iv" is intravenous, "sc" is subcutaneous, "RA" is psoriatic arthritis, and "AS" is ankylosing spondylitis. "UC" is ulcerative colitis, "CD" is ulcerative colitis, and "IBD" is inflammatory bowel disease, which may include ulcerative colitis and / or Crohn's disease. "PSO" is psoriatic arthritis, "PSA" is psoriatic arthritis, and "MS" is polysclerosis. In some implementations, the system described herein is not specific to a particular drug, but instead has one class or other subaggregate or group of drugs (eg, similar PK / PD). Can be applied to expected drugs, drugs known to be candidates for treating a particular medical condition, or other similarities). The development and application of drug-independent models allows for greater usefulness for a single model. Rather than implementing multiple models, each model corresponding to a single drug within a category, a drug-independent model is applicable to all drugs within that category, and therefore to a certain extent. It can be applied to a wider range of patients than a single drug model across routes of administration.

For example, the models described herein may include pharmacokinetic drug-independent models that can be used for all biologics used in the treatment of inflammatory diseases. Such models use the same model, such as those listed in Table 1, fully human monoclonal antibodies (mAbs), chimeric mAbs, humanized mAbs, fusion proteins, and mAb fragments (ie, different pharmacokinetics). It can be used to propose and / or test a dosing regimen for a range of drugs) with similar molecular weights and other similarities such as indications). The model may be used in a broad patient population, including inflammatory bowel disease, rheumatoid arthritis, psoriatic arthritis, psoriasis, multiple sclerosis, and other such diseases resulting from immunodysregulation. .. The development and application of drug-independent Bayesian models for agents in other broad drug sets (eg, aminoglycoside antibiotics, chemotherapeutic agents that cause low white blood cell counts, etc.) is similarly feasible. Drugs in the category include a variety of drugs such as subcutaneous, intravenous, oral, intramuscular, intrathecal, sublingual, oral, rectal, vaginal, intraocular, nasal, inhalation, spray, skin, or transdermal. It may be administered through the route. The drug-independent model may consider the route of administration by taking it as a variable input to the system and allowing greater flexibility for the model. Computational models such as the Bayesian model may be used to determine dosing regimen recommendations. For example, each iteration of the model may include the calculation or determination of a recommended dosing regimen. When additional data (such as physiologic parameter data or drug concentration data obtained from the patient) is available, another iteration of the model determines an updated recommended dosing regimen based on the additional data. It may be carried out as follows. The process may be repeated any number of times to reflect any new data describing the patient.

As used herein, a "dosing regimen" includes at least one dosage of a drug or class of drugs and a recommended schedule for administering to a patient at least one dosage of a drug. The dosage may be a multiple of the available dosage unit for the drug. For example, the unit of dosage available can be a single tablet, or a suitable piece of tablet that results when easily divided, such as half a tablet. In some implementations, the dosage may be a multiple of an integer of the available dosage units for the drug. For example, the unit of dosage available may be an indivisible 10 mg injection or capsule. For some routes of administration (eg IV and subcutaneous), any portion of dosing intensity can be administered. The recommended schedule is such that the predicted concentration-time profile of the drug in the patient in response to the first drug dosing regimen is at or above the target drug exposure or response level (eg, target drug concentration trough level) at the recommended time. Includes the recommended time to administer the next dosage of the drug to the patient.

Because the drug-independent model applies to a set of drugs, not just a single drug, the model provides patient-specific information when a patient is treated with multiple drugs in a set of drugs. May be reserved. For example, the set of drugs may include infliximab, vedolizumab, adalimumab, and other anti-inflammatory biologics. If the patient is treated with one drug (eg, infliximab) and then later with another drug (eg, vedrizumab), the model is once the patient is treated with a new drug. And, when determining the appropriate dosing regimen, all patient-specific data (drug concentration measurements, clearance rates, weight measurements, etc.) from the patient's infliximab-based treatment may be reserved. Reserving patient-specific data allows the drug-independent model to accurately predict the patient's ability to process the drug, thereby making more patient-specific medications when the patient changes drug therapy. Allows you to provide a regimen. Models should be trained on drugs and individual patients (eg, via Bayesian learning), as drug-independent models can accommodate a wide range of data and a wide range of diseases with multiple routes of administration. .. Such drug-independent pharmacokinetic models represent, for example, new applications of conventional population pharmacokinetic modeling. The ability to develop such drug-independent pharmacokinetic (PK) models is 1) a common general-purpose structural PK model for all drugs in the specific category, and 2) patient factors for PK parameters. Can be based on one or more of several factors, including similar effects of 3) similar indications. Therefore, the development and application of drug-independent Bayesian models for drugs within a wide range of other drug categories (eg, aminoglycoside antibiotics) is similarly feasible and within one category of drugs. Rather than implementing multiple models for each, it will allow for greater usefulness of a single drug-independent model. The development of specific "categories" for drug-independent modeling purposes is a novel means of classification or categorization of drugs.

Similarly, drug-independent models can be constructed for drug classes that exhibit commonality in pharmacodynamic effects (measured responses of drugs). For example, many chemotherapeutic agents cause neutropenia or low white blood cell count. This is generally a delayed response with a minimum white blood cell count that occurs 7-9 days after chemotherapy is administered. The effect of each drug on duration and the lowest point of white blood cell count can be different, but the underlying relationship between drug exposure and leukopenia is structurally similar and dependent on practical drugs. It allows a pharmacodynamic model that does not develop with respect to the class of chemotherapeutic agents that cause leukopenia.

In some implementations, drug-independent models account for pharmacokinetics and pharmacodynamics. The model comprises a PK component and a PD component that may be separate or interrelated within the model. For example, the PK and PD components may be interrelated so that the effect of PK on PD and the effect of PD on PK are included in the model. The PK component can include PK clearance parameters and the PD component includes PD response parameters. The interrelationship between the PK component and the PD component may be reflected by the PK clearance parameter, which is a function of the PD response, or vice versa. One or more differential equations can be used to account for patient response and drug clearance in the patient. The PD component of the model may consist of the first differential equation and the PK component of the model consists of the second differential equation. The first differential equation may represent the PD response by the patient and the second differential equation may represent the PK clearance by the patient. The first or second differential equation may include a PD response and / or PK clearance.

The systems and methods described herein may output recommended dosing regimens for certain classes of drugs without identifying specific drugs to be administered to the patient. As used herein, a "dosing regimen" may include a dosage of the drug and a recommended schedule for administering the dosage to the patient. The recommended schedule is as follows to the patient in order to achieve the predicted concentration time profile of the drug in the patient in response to the above, first drug dosing regimen, which is at the target, eg, drug concentration trough level, at the recommended time. Includes the recommended time to administer the dosage of the drug.

The drug category represents a group of drugs greater than one that exhibit at least one similar PK or PD effect, or share a common mechanism of action or some other similarity. For example, a similar PK effect may be a clearance within a specific range. Similar effects are either measured concentrations within a specific range, such as bioavailability, absorption, white blood cell count, blood concentration levels, or biomarkers / measurements discussed herein. May be good. The specific range may be within a 10-fold difference, i.e., values between 0.1 and 1 can be considered similar. The specific range may be defined by the user on the system interface. The drug is systemic inflammatory disease, or more specifically inflammatory bowel disease (IBD), ulcerative colonitis, Crohn's disease, rheumatic arthritis, ankylosing spondylitis, psoriatic arthritis, psoriasis, asthma, Alternatively, they may be grouped into categories according to the disease to be treated, such as multiple sclerosis. The drug category may also be based on drug structure. For example, the category may include monoclonal antibodies (mAbs), chimeric mAbs, fully human mAbs, humanized mAbs, fusion proteins, and / or mAb fragments. The category of agents may include anti-inflammatory compounds, chemotherapeutic agents, corticosteroids, immunomodulators, antibiotics, or biological therapies, or any other suitable group. The drug category can also be determined by the patient population, ie pediatrics, geriatrics. The drug category may also be determined by the user on the basis of other criteria, and the components of that category (or group) are electronically considered to be part of that category (or group) in the database. It may be specified as. The database is accessible to the systems and methods disclosed herein for use in determining category (or other group) based dosing regimens. A drug category or group may include variants of the same drug, such as the same drug with different routes of administration or different manufacturers. This feature can be particularly useful if the physician needs to compare a generic drug with a branded drug that varies in price, availability, indications, and / or routes. Many of the examples described herein relate to the pharmaceutical infliximab. However, the implementations described herein are immunosuppressive, anti-inflammatory, antibiotics, antibacterial, chemotherapeutic, anticoagulants, procoagulants, antidepressants, antipsychotics, psychostimulants, antidiabetic. It may be applied to a drug, an anticonvulsant, an analgesic, or any other suitable treatment.

Many of the implementations described herein relate to the treatment of IBD such as ulcerative colitis or Crohn's disease. There is no standard treatment regimen for IBD, but the following groups of drugs, namely anti-inflammatory compounds, corticosteroids, immunomodulators, antibiotics, or biological therapies, are used to treat IBD patients. Can be used. One recently developed treatment is a biological therapy (eg, a monoclonal antibody (mAb) such as infliximab) that targets, binds to, and inactivates an inflammatory protein called tumor necrosis factor (TNF). )including. In some cases, a combination of anti-TNF agents such as infliximab can be combined with one or more immunomodulators such as thiopurine. Such combination therapy can effectively reduce the rate of elimination (thus increasing the level of drug concentration in the patient's blood) and reduce the formation of anti-drug antibodies. The greatest challenge in treating patients with IBD is to ensure that they receive adequate exposure to treatment. The body presents several routes of "clearance" with respect to the drug. For example, patient metabolism can degrade mAbs by proteolysis (protein degradation), by cell uptake, and by the additional atypical clearance mechanism associated with IBD. For example, patients suffering from a medical condition such as focal segmental glomerulosclerosis (FSGS) due to the nature of the disease often suffer from excessive loss of the drug into the urinary tract or gastrointestinal tract. .. Also, in severe IBD, mAbs sometimes develop ulcers and are lost in the stool through the exfoliated mucosa, creating additional pathways for clearance. Overall, IBD patients are estimated to have a 40% to 50% higher infliximab elimination rate than other inflammatory diseases, making IBD particularly difficult to treat. The systems and methods described herein are also rheumatoid arthritis, psoriatic arthritis, ankylosing spondylitis, psoriasis vulgaris, low levels of coagulation factor VIII, hemophilia, schizophrenia, bipolar disorder, Dosing regimens may be developed to treat depression, bipolar disorder, infectious disease, cancer, seizures, transplants, or any other suitable distress.

Inputs into the system may be used to update and refine the model for specific patients taking specific drugs. Inputs into the system described herein may include concentration data, physiological data, and target responses. Inputs to the model generally include concentration data, physiological data, and target response. As discussed above, concentration data is one or more of the drugs in one or more samples obtained from a patient such as blood, plasma, urine, hair, saliva, or any other suitable patient sample. Indicates the concentration level of. Concentration data may reflect measurements of the concentration level of the drug itself in the patient sample, or another sample in the patient sample that indicates the amount of drug in the patient's body. The drug is a disease such as inflammatory bowel disease (including ulcerative bowel and Crohn's disease, IBD), rheumatoid arthritis, psoriatic arthritis, tonic spondylitis, psoriasis vulgaris, or any other suitable distress. Alternatively, it may be part of a treatment plan to treat a patient suffering from a particular health condition such as a disorder. Drugs used to treat such health conditions may include monoclonal antibodies (mAbs) such as infliximab or adalimumab. Although many of the examples described herein refer to treating IBD with infliximab, the systems and methods of the present disclosure lose their effectiveness over time in a measurable manner. It should be understood that it is applicable to any drug or treatment and can be used to treat any number of diseases, including any inflammatory disease such as IBD.

The inputs to the system are also the disease to be treated, the category of drug, the route of administration, the available dosage intensity, the preferred dosage (eg, 100 mg vial, 50 mg tablet, etc.), and the specific drug is entirely of human origin. Other drug information, such as whether it is present or not (eg, chimera), may be included. The drug information may be used to determine the treatment options available for the patient, the model selected, and the model parameters. For example, patients treated for IBD often have a higher clearance rate than patients without IBD, and drug dosing regimens for treatment with IBD must be adjusted accordingly. The preferred dosage may modify the dosing regimen before the regimen is recommended for the patient. For example, if the drug is available only in 100 mg vials, the recommended dosage may be rounded to the nearest 100 mg increment. In some implementations, drug information excludes information that identifies the drug currently being used to treat the patient. For example, drug data can be common in the drug category. Physiological data generally indicate one or more measurements of at least one physiological parameter of the patient. It includes medical record information, inflammation markers, drug disappearance indicators such as albumin or C-reactive protein (CRP) measurements, anti-drug antibody measurements, hematocrit levels, biomarkers of drug activity, body weight, Body size, gender, race, stage, disease status, previous therapy, previous clinical test results information, concomitant medications, complications, Mayo score, partial Mayo score, Harvey Bloodshaw index, blood pressure It may include at least one of a reading, psoriasis area, severity index (PASI) score, disease activity score (DAS), sharp / van der Heide score, and demographic information.

The targeted response may be selected by the physician based on the patient's tolerance to drug therapy and the physician's assessment of the response. In an example, the target response comprises the target drug concentration level of the drug in the sample obtained from the patient (maximum, minimum, or exposure window, etc.) and when the patient should accept the next dosage. , And may be used to determine the next dosage. The target drug concentration level is the target drug concentration trough level, the maximum target drug concentration, the target drug area under the concentration time curve (AUC), both the maximum target drug concentration and the trough, and the target drug such as blood pressure or coagulation time. It may include a dynamic endpoint, or any suitable metric of drug exposure. Targets may be determined by the physician based on drug data and / or concentration or response. In some implementations, the target may be automatically determined by the system to provide a therapeutic response in the patient. The system may evaluate multiple targets entered to determine one or more targets that result in a therapeutic response in a patient. The inputs described above (eg, concentration data, physiological data, drug information, and targeted responses) are used by the systems and methods of the present disclosure to personalize dosing regimen recommendations for patients.

Based on the input received, the systems and methods described herein are computational models that generate predictions of drug concentration-time profiles in patients (filed April 8, 2016, "Systems and Methods for". US Patent Application No. 15 / 094,379 (Application '379), published as US Patent Application Publication No. 2016-0300037, entitled "Patient-Special Dancing" (as a whole by reference). Set one or more parameter values for one or more of the model parameters described in). In some implementations, the computational model is a Bayesian model. For example, the computational model may develop patient-specific targeted dosing regimens, taking into account historical and / or current patient data. As discussed in Application '379, the computational model is for the synthesis and degradation rate of pharmacodynamic markers, which indicate the concentration-time profile of the drug, the pharmacokinetic components, and the individual response of the patient to the drug. It may include a pharmacodynamic component based on it. The computational model may be selected from a set of computational models that best fits the received physiological data. For example, if the patient is a 45 year old male, the system may select a computational model specific to males aged 30-50 years. The computational model can be personalized to a specific patient by taking into account patient-specific measurements (such as additional concentration data and additional physiological parameter data described herein).

A patient-specific drug dosing regimen may be provided as a function of a mathematical model (eg, a pharmacokinetic and / or pharmacodynamic model) that is updated to take into account the observed patient response. This is described in detail in Application '379, which is incorporated herein by reference in its entirety. In particular, the observed response of the specific patient to the initial dosing regimen is used to regulate the dosing regimen. The patient's observed response (eg, the observed drug concentration in the patient's blood) is a mathematical model and patient-specific characteristic to account for inter-object variability (BSV) that cannot be considered by the mathematical model alone. Used in conjunction with. The observed responses of the specific patient are used to refine the model and related inferences so that the model can be used to more accurately infer the expected response to the proposed dosing regimen for the specific patient. Can be used to effectively personalize. Thus, the observed patient-specific response data generally describes a typical patient response so that the patient-specific medication regimen can be predicted, suggested, and / or evaluated on patient-specific criteria. The model is used as "feedback" to adapt to a patient-specific model that can accurately infer patient-specific responses. Using the observed response data to tailor the model to an individual allows the model to be considered as a typical response only for a patient population or as a covariate within the model, with respect to a typical patient with certain characteristics. Explaining the "variable-typical" response, it allows it to be modified to take into account BSV, which is not taken into account in previous mathematical models.

The system and method may rely on Bayesian analysis. For example, Bayesian analysis may also be used to determine the appropriate dosage required to achieve the desired result, such as keeping the concentration of drug in the patient's blood near a particular level. good. Bayesian analysis can involve Bayesian inference and Bayesian updates. These Bayesian techniques are not only a function of patient-specific characteristics considered in the model as covariate patient factors, but are not considered in the model itself and are specific from the typical patient reflected by the model. It may be used to develop a model that distinguishes patients, reflects inter-subject variability (BSV), and is also a function of the observed patient-specific response. Thus, this disclosure has not been predicted based solely on variability between individual patients (eg, dosing regimens and patient factors) that has not been elucidated and / or considered by conventional mathematical models. Consider the patient response). In addition, the disclosure allows patient factors considered by typical models such as weight, age, race, laboratory test results, etc. to be treated as a persistent function rather than as a category (cutoff) value. .. By doing this, the model adapts to the specific patient so that patient-specific guessing and analysis can be performed to predict, suggest, and / or evaluate a personalized dosing regimen for the specific patient. Will be done.

It should be noted that this disclosure is not only for retroactive assessment of a medication regimen previously administered to a patient, but also for the proposed medication regimen prior to administration of the proposed medication regimen to the patient. It can also be used for proactive assessment or to identify dosing regimens (dosages, intervals, and routes of administration) for patients who will achieve the desired outcome. The Bayesian inference process may be used to test various dosing regimens for a patient as a function of the patient's specific characteristics considered as a patient factor covariate within the model and a mathematical model. This guess involves assessing the dosing regimen based on the expected response for a typical patient with patient-specific characteristics. In general, Bayesian inference involves the use of mathematical model parameters to infer the responses that a particular patient may present with different dosing regimens. Of note, the guessing step allows the determination of a likely patient response to the proposed dosing regimen prior to the actual dosing of the proposed dosing regimen. Therefore, the guessing step is to determine how each dosing regimen may affect the patient, as predicted by the patient-specific factors and / or data in the model / composite model. , A plurality of different proposed dosing regimens (eg, different dosages, dosing intervals, and / or routes of administration) can be used to test. Guessing may be compared to produce the best set of dosing regimens that are reasonably good for achieving therapeutic purposes or target exposures or concentration levels. For example, the target may be associated with maintaining trough blood levels above the therapy threshold.

In some implementations, the recommended dosing regimen provides confidence intervals that indicate the likelihood that a particular dosing regimen will be therapeutically effective for the patient. In particular, the confidence intervals for the response or concentration estimated from the individual data may be assessed based on the complexity of the model and the amount of individual data (PK data and / or PD data). In particular, the confidence intervals may reflect possible errors as individual prediction possibilities from the model. First, when individual measurements are not obtained from the patient, the model predictions have an error associated with them that is approximately equal to the unexplained variability of the PK and PD models. However, as individual measurements are obtained and introduced into these models, the error (or equivalent, confidence interval) will eventually approach the test error, which may correspond to the measurement error. Decreases to. Confidence intervals may also be provided to the clinical portal to give healthcare professionals a sense of the amount of error remaining within the model prediction.

The Bayesian update process may be used to update the model based on the patient's response to the medication regimen. In general, Bayesian inference involves Bayesian reasoning, which is a method by which Bayesian rules are used to update probability estimates for hypotheses as additional evidence is obtained. Bayesian updates are especially important in the dynamic analysis of data collected over time (sequentially). Methods as applied herein use a model that not only describes the time course of exposure and / or response, but also includes terms that describe the unexplained (random) variability of exposure and response. The result of the Bayesian update is a set of parameters conditioned on the observed data. The process involves sampling parameters from a previous variance (eg, the underlying model) and calculating the expected response based on the underlying model. For each underlying model, the differences between the expected value of the model and the observed data are compared. This difference is referred to as the "objective function". The parameters are then adjusted based on the objective function and the new parameters are tested against the observed data by comparing the difference between the expected value of the new model and the observed data. The process is repeated until the objective function is minimized and the parameters that minimize the objective function suggest that it best describes the current data. In some implementations, a random function may be used to intervene some variation and ensure that the drug-independent minimum of the objective function is obtained.

In some cases, the systems and methods of the present disclosure are based on how the patient responds to previous treatment methods from within (or other groups) of the drug or beyond. Determine regimen recommendations. As an example, when a patient does not respond to treatment with one drug (eg, a previously administered drug), the physician sometimes turns the patient into a different drug (eg, the currently administered drug). Switching, drugs can be interrelated (eg, from the same category that shares a common mechanism of action). Lack of response to treatment is sometimes referred to as a "failure" of treatment that can occur in patients with high disappearance rates. In these patients, the drug is often excreted from the body before the full beneficial effects of the drug are realized by the body. Generally speaking, IBD patients with a high elimination rate for one drug (previously administered) also have a similar clearance mechanism for similar drugs in general, so another similar drug (still administered). It can be expected to have a high disappearance rate even for (not). Thus, the rate of disappearance of a previously administered drug (as reflected by the measured drug concentration level) may signal the rate of disappearance of the drug to be administered. The systems and methods described herein utilize this correlation in determining a recommended dosing regimen for another drug (eg, a drug currently used to treat a patient). Historical patient data with one drug (eg, previously administered drug) may be used. For example, a doctor may prescribe adalimumab for a patient. Patients may have a higher than average clearance for adalimumab. If the patient fails adalimumab-based therapy, the physician may then try therapy with infliximab. The systems and methods described herein may then allow the patient to clear infliximab at a similarly higher rate due to the fact that both adalimumab and infliximab are mAbs with similar clearance mechanisms. Consider higher clearance than the average for adalimumab when calculating the dosing regimen for infliximab.

The systems and methods described herein use an iterative approach to determine and provide recommended dosing regimens. In the examples, an initial dosing regimen (determined based on the patient's available information and the physician's experience) is administered to the patient. Data indicating the patient's response or response to the initial dosing regimen, such as the patient's physiological and / or concentration data, are provided to the system as feedback on the initial dosing regimen. All, part, or none of that data is then used as input to the computational model, which is provided to the physician as a recommendation, to calculate the updated dosing regimen. The physician may choose to administer the dosing regimen exactly as recommended, or the physician may choose to slightly modify it before administering the recommended dosing regimen. good. For example, the recommended dosing regimen may include a specific dosing interval (eg, 4 weeks) and a specific dosage (eg, 1.9 vials). The physician will modify the regimen to suit the patient's schedule (eg, if the patient can only return to another dosage after 4 weeks and 1 day), into a specific number of vials. You may choose to round up (eg, into two vials) or both. This iterative approach is described in detail below.

Using the computational model and set parameters described above, the systems and methods described herein determine a first medicinal dosing regimen for a patient. The first pharmaceutical dosing regimen comprises at least one dosage of the drug and a recommended schedule for administering the patient at least one dosage of the drug. The recommended schedule is next to the drug so that the predicted concentration time profile or pharmacodynamic marker profile of the drug in the patient in response to the first drug dosing regimen is at or above the target drug exposure level at the recommended time. Includes the recommended time to administer the dosage of. In some implementations, the dosing regimen may be displayed (eg, through the user interface). In some implementations, the physician may receive or view the first dosing regimen (eg, through the user interface). The physician may decide to modify the medication regimen before it is administered to the patient. Once the physician has determined the course of treatment, the dosage may be administered to the patient and additional data (indicating how the patient is responding to the dosage) is received.

In some implementations, at least in part, based on the first medicinal medication regimen to the patient, after initiation of administration of the medication regimen, the systems and methods described herein are additional acquisitions from the patient. Receive concentration data as well as additional physiological data. Your healthcare professional may choose to administer the dosing regimen to the patient exactly as recommended by the system. However, healthcare professionals may also choose to modify the first medicinal dosing regimen prior to administration of the drug. For example, the healthcare professional may choose to round up or down the dosage, or may choose to modify the dosing time to better fit the patient and physician's schedule, first. Any suitable modification to the pharmaceutical dosing regimen may be made, or any suitable combination thereof may be made. For example, the first medicinal dosing regimen may set a dosage of 100 mg on 20 April, but the physician may decide to order 90 mg on 22 April. Details of the dose administered can then be entered into the system. The system may then receive additional data (eg, concentration and physiological parameter data) showing how the patient is responding to the dose administered.

Often, when the patient is in the early stages of treatment, not much patient data is present. In particular, historical data showing how a patient responded to or responded to different dosages of the drug is typically not available. In this case, when there is little patient data to rely on, the systems and methods of the present disclosure can be the number of concentration data points considered in the iteration of the model, a single data point that can be the most recent data point. It may be limited to points only. The most recent data points are such that the data points most accurately reflect the patient's current status and allow the systems and methods of the present disclosure to heavily weight the data points in determining recommended dosing regimens. It may be the only density data entered into the model. Thus, the input to the model is whether the cycle in which the treatment of the first drug dosing regimen is administered exceeds a certain percentage of the total time of the first drug dosing regimen (eg, the patient is in the early stages of treatment). It is determined based on whether or not it is in.

The systems and methods described herein may display information to the user. The system may display information through a user interface to which the physician or other users can interact. For example, the user interface may display dosing regimens (eg, as shown in FIGS. 7A and 7B and described below), or may display concentration and / or physiological parameter data. In some implementations, the user can switch or otherwise select the data to be viewed on the user interface. In some implementations, the predicted concentration-time profile of the drug in the patient in response to the first drug dosing regimen, as generated by the computational model, is displayed. Concentration data and additional concentration data, physiological and additional physiological data, target drug exposure levels, and at least a portion of the recommended time as the point at which the predicted concentration-time profile of the drug intersects the target drug exposure level. The indication may also be displayed. For example, a physician can use a graphical user interface to enter targeted responses for a patient, enter patient data (eg, physiological and concentration data), and view predicted concentration-time profiles for patients who responded to different dosing regimens. You may.

In some implementations, inputs such as the inputs described above are read from electronic medical records. For example, the physician may then communicate with an electronic database system (locally stored in the medication system or remotely) that holds multiple patient information, including health information about the patient, into the medication system. You may enter the patient's name. The health information of the electronic database system may include, for example, the current drug being taken by the patient. The dosing system may then receive information about the patient's health and / or drug information from the electronic database system. In some embodiments, drug information (such as drug category, route of administration, minimum dosage, or any other suitable information) is read from a separate database or server once the drug name is identified. .. Such systems are advantageous, for example, in reducing the time required for clinicians to manually enter information and reducing the chance of input errors by automatically retrieving information from these databases. Can be. Such EMR applications can be activated or deactivated within a given system, depending on circumstances and needs.

The systems and methods described herein can be used to predict patient drug clearance for one class (or other group) of drug. Such a model may be standardized to take into account differences between drugs within a group of drugs. In some implementations, the model is generated by collecting parameter values from a set of published models that correspond to the drug category. Parameter values may be collected in the look-up table. Parameter values may be converted to "standardized values" so that they can be compared or pooled within a drug-independent model. This is for patient populations for open models with covariate effects, such as those published by the system, and for extended open models with all measurements and expected covariate effects. Allows you to simulate PK characteristics with respect to. Standardized parameters include body weight, albumin, ADA negative, the presence of immunosuppressants, CRP, glucose, human-derived or chimeric, non-IBD disease, gender, nonlinear clearance, and CL. It may be included. Look-up tables may be used to normalize the parameters to allow preliminary inference from drug-independent models. The look-up table may be manipulated by the user through the user interface or stored in the model database 606D of FIG. The look-up table may be structured so that a subset of the table can be sent to a simulation function in the program so that each drug can be easily simulated in different scenarios. With respect to the pooled data for that group of drugs, the concentration simulated from the normalized parameters for each drug within the group of drugs will fit the simulated concentration data to the pooled data. It may be compared and analyzed. A drug-independent model for a group of drugs provides a set of parameters that apply to or represent all the drugs within that group.

FIG. 1 shows process 100, implemented by a system or method for providing a dosing regimen for multiple drugs (also referred to as a set of drugs), according to an exemplary implementation. In step 102, the input to the system is received. Inputs include drug sets and routes of administration, concentrations or response data, as well as drug data indicating target drug exposure or response levels. In some implementations, drugs within a set of drugs are expected to exhibit similar PK behavior, similar PD behavior, or both. In some implementations, drug data includes indications such as the intended patient population or disease for each drug. In some implementations, drug data contains information about the structure or function of the drug, such as its mechanism of action. Information about the structure of a drug can be obtained, for example, if one or more drugs in the set of drugs are antibodies, each antibody is chimeric, fragmented, of human origin, or wholly human. It may include whether or not it is derived. Drug data is supplied without identifying the specific drug, and the physician can simulate the results without identifying or constraining the outcome to the specific drug, eg, predicted concentration time profile, clearance, and /. Alternatively, it is possible that it may be possible to obtain a medication regimen. The concentration or response data may indicate the specific drug concentration or response level of the set of drugs in the sample obtained from the patient. Concentration or response data may further indicate the concentration or response level of the specific drug administered using the specific route of administration in the sample obtained from the patient. Patient-specific covariates or measurements such as body weight, albumin, ADA negative, presence of immunosuppressive agents, CRP levels, glucose levels, complications, disease status, and gender may also be included as inputs to the system. good.

In step 104, the mathematical model is selected from a database stored in memory (eg, the model database 606D of FIG. 6). The database is processor-based and stores multiple mathematical models. A mathematical model represents a response by multiple patients to multiple drugs in a set of drugs, each response of a response represents a patient response to at least one drug in a set of drugs, and a mathematical model represents a particular drug. Not unique to. The mathematical model may be selected based on one or more of the inputs to the system.

In some implementations, the mathematical model is selected based on drug data. If the model is selected based on drug data, the selection step may involve comparing the drug data with the parameters or covariates of the model. This step may involve maximizing the number of similar parameters between the model and the set or category of drug. Each model in the database may be associated with an error or confidence interval that indicates the model's previous performance, and the model may be selected based on the error or confidence interval. The model may be selected based on the amount of data or information available or applicable to the model. Use the widest range of information or data available so that the selected model can be refined to make more accurate predictions and provide recommendations that are more likely to reach the target. However, it would be advantageous. For example, the concentration or response data and / or the type of concentration or response data can be analyzed and incorporated into the model, or at least a portion or maximum of that data can be incorporated. You may choose a model that is possible. In some implementations, the selection may be performed by an optimization function. Instead of, or in addition to, the selection method discussed above, the Bayesian method may be used to select the model. In some implementations, multiple models may be compared against each other and the model that best represents the patient or input may be selected. In some implementations, the model is a Bayesian model in which it may be possible to perform Bayesian analysis such as Bayesian inference.

In some implementations, parameters may be set for computational models that generate predictions of drug concentration-time profiles in patients. The parameters are set based on the received input in step 102. In some implementations, the computational model is a Bayesian model. In some implementations, the computational model is based on the synthesis and degradation rate of pharmacodynamic markers, showing the pharmacokinetic component and the individual response of the patient to the drug, showing the concentration-time profile of the drug. including. In some implementations, process 300 includes additional optional steps and the computational model is selected from a set of computational models that best fits the received physiological data. In some implementations, the computational model considers historical data showing the patient's response to past drugs in order to generate predictions of the drug concentration-time profile in the patient. In some implementations, past drugs may belong to the same class of drugs as current drugs. In some implementations, past drugs may belong to a different class of drugs than current drugs.

In step 106, a plurality of predicted concentration time profiles are inferred. The predicted concentration-time profile is estimated using the selected mathematical model and based on concentration data and route of administration. Each Predicted Concentration Time Profile for Multiple Predicted Concentration Time Profiles corresponds to a dosing regimen within multiple dosing regimens. Each dosing regimen of the plurality of dosing regimens may include at least one dosage and a recommended schedule for administering the at least one dosage to the patient. In some implementations, the recommended schedule is determined to maintain the target concentration or exposure or response level based on at least one dosage or concentration or response data using the model. Step 106 may be accompanied by Bayesian inference.

In step 108, the first dosing regimen for the set of drugs is selected. The first dosing regimen is presumed to achieve therapeutic objectives based on target drug exposure or response level. The first dosing regimen may be output from the system. In some implementations, the first dosing regimen is output for display on the user device. In some implementations, multiple first dosing regimens are output. In some implementations, the physician may select a first dosing regimen from a plurality of first dosing regimens. The choice of the first dosing regimen may be determined by predetermined criteria or by the score associated with each dosing regimen within the plurality of dosing regimens. For example, the user may select a dosing regimen that best meets the therapeutic objectives or best fits the target concentration or concentration time profile. The score may be a percentage, calculated by the system, that represents the closeness to which the predicted concentration-time profile for a particular dosing regimen fits the target. The score may be a p-value known in the latest art. The score may indicate a residual error. For example, the score may be a mean residual error or may be presented as a confidence interval associated with a simulation using a particular dosing regimen. These scores may be output with the corresponding dosing regimen.

In one embodiment, depending on viewing the selected / recommended first dosing regimen via the user interface, the healthcare professional chooses to administer the first dosing regimen as recommended. May, or the healthcare professional may change one or more dates or times within the recommended schedule to adjust to the patient's or healthcare professional's schedule, and / or (eg, in a vial). You may choose to slightly modify the recommended dosing regimen, such as by changing the dose to the closest integer, eg, by rounding up. The dosing regimen (eg, either the recommended dosing regimen or a modified version of the recommended dosing regimen) may be, for example, an oral drug, intravenous, intramuscular, intrathecal, or subcutaneous injection, rectal or vaginal insertion, infusion, It is administered by a medical practitioner through topical, nasal, sublingual or intraoral application, inhalation or spraying, intraocular or intraocular route, or any other suitable route of administration. The dosage may be a multiple of the available dosage unit for the drug. For example, the unit of dosage available can be a single tablet, or a suitable piece of tablet that results when easily divided, such as half a tablet. In some implementations, the dosage may be a multiple of an integer of the available dosage units for the drug. For example, the unit of dosage available may be an indivisible 10 mg injection or capsule. For some routes of administration (eg IV and subcutaneous), any portion of the dosage intensity can be administered.

The healthcare professional (or another user of the system) may provide the system with data indicating the actual dosing regimen administered. In one embodiment, if the administered dosing regimen is identical to the recommended dosing regimen, the user simply selects a button on the user interface indicating that the recommended dosing regimen has been selected to be administered. You may. Alternatively, if the dosing regimen administered is different from the recommended dosing regimen, the user will provide the system with data indicating the dosing regimen administered. After starting administration of a dosing regimen to a patient (which may be the same as or a modified version of the recommended dosing regimen provided by the model), the system responds to the patient's observed response to the dosing regimen. Receive additional data as shown. In particular, the system may receive additional concentration data, additional physiological data, or both. The system receives additional concentration data linked to the concentration data. For example, additional concentration data can represent a patient's response to an administered dosing regimen and may be a single data point or multiple data points. In addition, or as an alternative, the system receives additional physiological parameter data linked to concentration data. For example, additional physiological parameter data may represent a patient's response to an administered dosing regimen and may be a single data point or multiple data points in the form of a vector or matrix. The system may receive additional concentration data without receiving additional physiological parameter data. Alternatively, the system may receive additional physiological parameter data without receiving additional concentration data. In another embodiment, the system may receive both additional concentration data and additional physiological data.

The method may include additional steps. In some implementations, the additional step updates the mathematical model based on the step of receiving additional drug data, which indicates the updated route of administration for the specific drug, and the updated route of administration for the specific drug. Steps to calculate at least one updated dosing regimen to reach the therapeutic objective for the patient, and at least one updated for the patient, based on the updated mathematical model. Includes a step to output the medication regimen. In such implementations, these additional steps allow the physician to identify the route of administration to provide the best treatment to the patient, i.e., to best meet the therapeutic objectives. Allows analysis of the entire set of drugs while varying. In some implementations, the additional step is to receive additional patient data indicating the patient's response to specific drug administration according to the first dosing regimen or a modified version of the first dosing regimen. Yet, the additional patient data comprises the additional concentration data, which indicates the concentration level of one or more of the specific drugs in one or more samples obtained from the patient, and the first step. Steps to update the mathematical model based on the patient's second response to specific drug administration, and the patient's based on the updated mathematical model, according to the medication regimen or a modified version of the first medication regimen. Includes a step of calculating at least one updated dosing regimen to reach a therapeutic objective for the patient and a step of outputting at least one updated dosing regimen for the patient. The step to update may be accompanied by a Bayesian update.

In some implementations, the system also receives certain drug constraints such as available dosage intensity, preferred dosage intensity, available dosage unit, manufacturing schedule, vial strength, or price as input. These constraints may be incorporated within the steps described above. For example, the first dosing regimen may be generated to include a dosage that is a multiple of the available dosage unit. The dosing schedule in the dosing regimen may be configured to be a multiple of the manufacturing schedule. The system may be configured to determine the dosage that best meets the therapeutic objective at the lowest cost to the patient, based on the drug cost entered. The route of administration is at least one of subcutaneous, intravenous, oral, intramuscular, intrathecal, sublingual, intraoral, rectal, vaginal, intraocular, nasal, inhalation, spray, skin, or transdermal. May be. The multiple drugs may be one of monoclonal antibodies and / or antibody constructs, cytokines, drugs used for enzyme replacement therapy, aminoglycoside antibiotics, and chemotherapeutic agents that cause leukocyte depletion.

In some implementations, each drug in multiple drugs shares a similar chemical structure. Each drug among the plurality of drugs may share a similar mechanism of action. In some implementations, the drug treats inflammatory diseases such as inflammatory bowel disease (IBD), rheumatoid arthritis, ankylosing spondylitis, psoriatic arthritis, psoriasis, asthma, and multiple sclerosis. Used for. In one implementation, the specific drug is one of the drugs listed in Table 1. Drug data may be included within a mathematical model or may explain similarities or differences between drugs within multiple drugs, to meet parameters, dosing intensity with respect to the specific drug, and / or the specific drug. May include an indicator indicating whether the drug is entirely human-derived, chimeric, or fragmented. The system presents patient data indicating a patient disease to be treated, eg, to classify drugs applicable to the patient disease, or to retrieve information related to the patient disease used in a model or various steps. May be received as an input.

In some implementations, the input received comprises physiological data indicating one or more measurements of at least one physiological parameter of the patient. At least one physiological parameter of the patient is an inflammation marker, albumin measurement, drug clearance indicator, C-reactive protein (CRP) measurement, anti-drug antibody measurement, hematocrit level, biomarker of drug activity, Weight, body size, gender, race, stage, disease status, previous therapy, previous clinical test results information, concomitant medications, complications, Mayo score, partial Mayo score, Harvey Bloodshaw index , Blood pressure reading, psoriasis area, severity index (PASI) score, disease activity score (DAS), sharpness score, and at least one of demographic information. A mathematical model may be selected from a set of mathematical models to best fit the received physiological data.

In some implementations, the method, for display, is of a patient-specific concentration-time profile and concentration data and additional concentration data showing the patient's response to multiple drugs in response to the first dosing regimen. Includes steps to generate at least some of the indications. Target drug exposure or response level indications may occur for display purposes. In some implementations, the system receives historical data showing the patient's response to a second drug other than the specific drug. Computational models may consider historical data to generate predictions of concentration-time profiles of multiple drugs in a patient. Therefore, the model may be able to use a wider range of data, such as data collected from administration of drugs other than the specific drug, to generate predictions about patient response to multiple drugs. Drug-independent models use observed patient response data or historical drug data, such as clinical trial data, to predict patient response to medication regimens and recommend optimized medication regimens. It can complement the lack of sex.

In some implementations, the system receives, as input, initial drug concentration input data representing the initial comparative concentration data points. In some cases, the user or physician may not have access to the measured concentration or response data, instead the system estimates the first point based on the physiological data. Or you have to calculate. In some cases, the initial comparative concentration data points indicate the specific drug concentration level of the multiple drugs in the sample obtained from the patient. In some cases, initial comparative concentration data points are calculated based on the patient's physiological parameters. The first dosing regimen may be calculated based on an estimate of the predicted concentration-time profile based on input data obtained from selected mathematical models and specific patient physiologic parameters.

FIG. 2 is a block diagram of a computing device for performing any of the processes described herein. Each of the components of these systems may be implemented on one or more computing devices 200. In one aspect, multiple components of these systems may be contained within a single computing device 200. In some implementations, components and storage devices may be implemented across several computing devices 200.

The computing device 200 includes at least one communication interface unit, an input / output controller 210, a system memory, and one or more data storage devices. The system memory includes at least one random access memory (RAM202) and at least one read-only memory (ROM204). All of these elements communicate with the central processing unit (CPU 206) to facilitate the operation of the computing device 200. The computing device 200 may be configured in many different ways. For example, the computing device 200 may be a traditional stand-alone computer, or, as an alternative, the functionality of the computing device 200 may be distributed across multiple computer systems and architectures. In FIG. 2, the computing device 200 is connected to another server or system via a network or a local network.

The computing device 200 may be configured in a distributed architecture in which the database and processor are housed in separate units or locations. Some units perform primary processing functions and include, at a minimum, a general controller or processor and system memory. In a distributed architecture implementation, each of these units acts as a primary communication link with other servers, clients or user computers, and other related devices via the communication interface unit 208, a communication hub or port (Figure). It may be attached to (not shown). The communication hub or port may itself have minimal processing power, primarily acting as a communication router. Various communication protocols, including, but not limited to, Ethernet, SAP, SASTM, ATP, BLUETOOTH (registered trademark) TM, GSM (registered trademark), and TCP / IP are part of the system. You may.

The CPU 206 includes one or more conventional microprocessors and the like, and one or more auxiliary coprocessors such as a mathematical coprocessor for reducing the workload from the CPU 206. The CPU 206 communicates with the communication interface unit 208 and the input / output controller 210, through which the CPU 206 communicates with other devices such as other servers, user terminals, or devices. The communication interface unit 208 and the input / output controller 210 may include, for example, a plurality of communication channels for simultaneous communication with other processors, servers, or client terminals.

The CPU 206 also communicates with the data storage device. The data storage device may include an appropriate combination of magnetic, optical, or semiconductor memory, and may include, for example, an optical disk such as RAM202, ROM204, flash drive, compact disk, or a hard disk or drive. The CPU 206 and data storage device are located entirely within, for example, a single computer or other computing device, or USB ports, serial port cables, coaxial cables, Ethernet cables, telephone lines, high frequency transmission / reception, respectively. They may be interconnected by a machine, or other similar wireless or wired medium, or a communication medium such as the combination described above. For example, the CPU 206 may be connected to a data storage device via the communication interface unit 208. The CPU 206 may be configured to perform one or more specific processing functions.

The data storage device may be attached to the CPU 206, for example, according to (i) the operating system 212 for the computing device 200, (ii) the systems and methods described herein, in particular according to the processes described in detail with respect to the CPU 206. To store information that can be used to store information required by one or more applications 214 (eg, computer program code or computer program products), or (iii) programs that are adapted to direct. The database 216 adapted to may be stored.

The operating system 212 and application 214 may be stored, for example, in compressed, uncompiled, and encrypted forms, and may include computer program code. The instructions of the program may be read from a computer-readable medium other than the data storage device into the main memory of the processor from the ROM 204 or from the RAM 202 or the like. Execution of a sequence of instructions in a program causes the CPU 206 to perform the process steps described herein, but the wired network replaces or combines software instructions for implementing the processes of the present invention. May be used. Therefore, the systems and methods described are not limited to any specific combination of hardware and software.

Suitable computer program codes may be provided to perform one or more functions described herein. The program also allows the operating system 212, database management system, and processor to interface with computer peripheral devices (eg, video display, keyboard, computer mouse, etc.) via the input / output controller 210 "device driver". It may include a program element such as ".

As used herein, the term "computer-readable medium" provides instructions to the processor of computing device 200 (or any other processor of the device described herein) for execution. Or refers to any non-transient medium involved in providing. Such media can take many forms, including, but not limited to, non-volatile and volatile media. Non-volatile media include, for example, optical, magnetic, or magneto-optical disks, or integrated circuit memory such as flash memory. Volatile media typically include dynamic random access memory (DRAM), which forms the main memory. Computer-readable media of common form include, for example, floppy disks, flexible disks, hard disks, magnetic tapes, any other magnetic media, CD-ROMs, DVDs, any other optical media, punch cards, paper tapes, holes. Any other physical medium with a pattern, RAM, PROM, EPROM or EEPROM (electronically erasable programmable read-only memory), FLASH-EEPROM, any other memory chip or cartridge, or any other computer-readable Includes non-transient media.

Various forms of computer-readable media are involved in carrying one or more sequences of one or more instructions to the CPU 206 (or any other processor of the device described herein) for execution. You may. For example, the instructions may initially be held on a magnetic disk of a remote computer (not shown). The remote computer can load the instructions into its dynamic memory and send the instructions over an Ethernet® connection, cable line, or even a telephone line using a modem. A communication device 200 (eg, a server) located locally on the computing device can receive the data on a separate communication line and place the data on the system bus for the processor. The system bus carries data to main memory, where the processor reads and executes instructions. Instructions received by the main memory may optionally be stored in memory either before or after execution by the processor. In addition, the instructions may be received via the communication port as electrical, electromagnetic, or optical signals, which are exemplary forms of wireless communication or data streams that carry various types of information.

3A and 3B are graphs of predicted concentration time profiles for a specific patient in response to a drug-specific dosing regimen within a category of drug. In each graph, concentration-time profiles for a specific patient are simulated for the drug category using a drug-independent model such that each profile corresponds to a drug within the drug category. The class of drugs simulated here includes various mAbs that share some commonalities as described above. In this exemplary embodiment, the dosing regimen is the standard dosage and interval recommended for each drug in the package insert. In particular, FIG. 3A shows a predicted concentration time profile for the drug category. One of the standard dosing regimens applied is shown in the chart above as a regimen containing 10 mg etanercept (“ETAN”) administered intravenously in a single dose. When concentrations are allowed to be reduced to very low (below therapeutic doses) values, for example, if the patient does not comply with the dosing regimen, the profile is significant due to differences between the drugs. Will be in a different state. These differences are reflected within the drug-independent model by standardized parameters and may include certain aspects of drug-related pharmacokinetics and / or pharmacodynamics.

FIG. 3B shows a predicted concentration time profile for the drug category. One of the standard dosing regimens applied is shown in the chart above as a regimen containing 150 mg saliumab (“SARI”) administered subcutaneously every other week. The profiles depicted in FIG. 3B are relatively similar and at least follow similar trends and maintain higher concentrations over time. For example, the simulation may represent a patient who is compliant with a medication regimen. Therefore, differences and general variability between drugs can be minimized by controlling the patient at reasonable drug concentrations. Volatility arises between drugs within the drug category only when the dosing regimen is not followed or when the dosing regimen is inadequate.

FIG. 4 illustrates a flow chart illustrating an exemplary method 400 for developing a drug-independent model, as described herein. Step 402 involves generating a list of over-the-counter drugs for treating a disease or patient population. These diseases or patient populations may be any of those described above or described in any disease or patient population targeted by over-the-counter drugs. For example, the list of over-the-counter drugs may be sourced from literature or regulatory bodies such as medical magazines or the Food and Drug Administration (FDA). Table 1 may be an exemplary list of such drugs. Step 404 involves defining drug information. The drug information may include routes of administration, structures, indications, constraints, PK / PD parameters, or other data described herein, as discussed similarly for step 102 of FIG. This information may be sourced from the same source as the list of drugs or may be typed in by the user. Step 406 involves obtaining a clinical pharmacological summary of the over-the-counter drug. Pharmacological information is used to account for similarities or differences between over-the-counter drugs in drug-independent models. Step 408 involves extracting population PK model parameters. Step 410 involves simulating a concentration-time profile for each drug. Step 412 involves pooling the simulated data and fitting the data to one model, i.e., a drug-independent model. Step 414 involves comparing the simulation from one model with the original simulation. Step 416 involves further refining one model using the drug information from step 404. Step 418 involves comparing the original simulation with one refined model. Step 420 involves further refining one model based on the dosage used, eg, based on the concentration or response data described in connection with step 102 of FIG. In step 422, the final drug-independent model is provided.

In some implementations, in connection with step 408, a model is generated by collecting parameter values for a set of published models corresponding to the drug category. It should be appreciated that step 408 may involve, as an alternative, or in addition, a step of extracting population PD model parameters. In implementations where the PD model is desired, response data and response targets may be used in place of concentration data and concentration targets. Parameter values may be collected in a look-up table or in the database described below in FIG. The look-up table may be manipulated by the user or automatically by the system as needed. The look-up table may be located anywhere in the system infrastructure, such as the system described in FIGS. 2 and 6. Parameter values may be converted to "standardized values" so that they can be compared or pooled within a drug-independent model. This is in step 410 for a patient population for a public model with covariate effects such that the system is exposed, and for an extended public model with all of the measured and assumed covariate effects. Allows simulation of PK and / or PD characteristics for a patient population. Look-up tables may be used to normalize parameters and allow preliminary inference from drug-independent models. The normalization process may occur during steps 412 or 416. The look-up table is structured so that the Cebu set of the table can be sent to a simulation function in the program so that each drug can be easily simulated, for example, in various scenarios during step 410. May be good.

As described in step 414, the simulated concentration from the parameters normalized for each drug within the group of drugs is such that the simulated concentration data fits the pooled data of the drug. Pooled data for that group may be compared and analyzed. A drug-independent model for a group of drugs provides a set of parameters that apply to or represent all drugs within that group, and this feature represents a significant technical contribution. Another technical effect of the drug-independent model is realized in step 420, where the data collected from the dose administered can be used to further refine the model. Since the model is specific drug independent, the data from the dosage used can be from any drug within the drug category and the drug within the drug category is drug independent. To share the similarities reflected within the model parameters, the model will receive that data and will be further refined, for example, through a Bayesian update.

FIG. 5 shows process 500 for using data from a patient's response to previous treatments to inform them of recommended dosing regimens for different treatments, with an exemplary implementation. Process 500 can be performed using the computerized system 600 of FIG. 6 or any other suitable computerized system. In step 502, the input to the system is received. The inputs indicate previous concentration data and one or more measurements of at least one physiological parameter of the patient, indicating one or more previous concentration levels of the previous drug in one or more samples obtained from the patient. Physiological data indicating the values and the target drug exposure or response level of the current drug are included. Patients may have failed previous drug-based therapies. Medical information (eg, concentration data) that indicates the patient's response to the previous drug can be useful in determining how the patient will respond to the current drug. In some implementations, previous and current drugs may belong to the same category or have similar effects. For example, the previous and current drugs may be different monoclonal antibodies. If the patient's data indicate that the patient has a high elimination rate with respect to the previous drug (eg, the patient's body excludes the previous drug from the patient's system at an average or higher rate than expected). Patients are likely to have a similarly high elimination rate with respect to current drugs. By reserving information from previous drugs, the systems and methods described herein rapidly determine a personalized dosing regimen for the current drug, thereby allowing the patient to switch drugs. The amount of untreated time can be reduced.

In step 504, parameters are set with respect to the computational model that gives rise to the prediction of the current drug concentration-time profile in the patient. Computational models are Bayesian models, PK / PD models, of the models described above in connection with FIGS. 1 and 4, and below in connection with FIG. 6 (eg, stored in the model database 606D). It may be any of these, or any suitable model. The parameters are set based on the received input in step 502. In some implementations, the computational model is a Bayesian model. In some implementations, the computational model is a pharmacokinetic component that indicates the concentration-time profile of the drug and a pharmacodynamic component based on the synthesis and degradation rate of the pharmacodynamic marker that indicates the individual response of the patient to the drug. including. In some implementations, Process 1500 includes an additional optional step in which the computational model is selected from a set of computational models that best fits the received physiological data.

In step 506, a first medicinal dosing regimen for the patient is determined using a computational model and set parameters. The first pharmaceutical dosing regimen comprises (i) at least one dosage of the drug and (ii) a recommended schedule for administering the patient at least one dosage of the drug. The recommended schedule is that the predicted concentration-time profile of the drug in the patient in response to the first drug dosing regimen is at the target drug concentration trough level at the recommended time, as described above, the next dosage of the drug is administered to the patient. Includes recommended time to do. At step 508, additional concentration data and / or additional physiological data corresponding to the current drug are obtained from the patient. Additional concentration and additional physiological data can result from administration of the first pharmaceutical dosing regimen to the patient. In some implementations, process 500 includes additional steps that provide a display of various system inputs and outputs. In some implementations, process 500 passes through the predicted concentration-time profile of the drug in the patient in response to the first drug dosing regimen, as generated by the computational model, and (eg, through the user interface 622 in FIG. 6). Provide at least a portion of the concentration data for display as well as the additional concentration data. In some implementations, indications of the target drug concentration trough level are provided for display (eg, through the user interface 622 in FIG. 6).

At step 510, the concentration data is updated to include both the concentration data of step 502 and the additional concentration data of step 508. In some implementations, in step 510, the original concentration data from step 502 is excluded and the additional concentration data becomes the concentration data. In step 512, the parameters for the computational model are updated based on the updated inputs, and in step 514, the model iterations determine a second medicinal dosing regimen for the patient based on the updated parameters. It is carried out like this.

FIG. 6 shows a block diagram of a computerized system 600 for implementing the systems and methods disclosed herein. In particular, the system 600 uses drug-specific mathematical models and observed patient-specific responses to treatment to predict, propose, modify, and evaluate suitable drug treatment regimens for specific patients. The system 600 includes a server 604, a clinical portal 614, a pharmacy portal 624, and an electronic database 606, all connected via network 602. Server 604 includes processor 605, clinical portal 614 includes processor 610 and user interface 612, and pharmacy portal 624 includes processor 620 and user interface 622. As used herein, the term "processor" or "computing device" refers to hardware, firmware, and to perform one or more of the computerization techniques described herein. Refers to one or more computers, microprocessors, logical devices, servers, or other devices that are configured with software. Processors and processing devices may also include inputs, outputs, and one or more memory devices for storing data currently being processed. An exemplary computing device 200, which may be used to implement any of the processors and servers described herein, is described in detail above with reference to FIG. As used herein, a "user interface" is, but is not limited to, one or more input devices (eg, keypads, touch screens, trackballs, voice recognition systems, etc.) and / or one or more. Includes any suitable combination of output devices (eg, visual display, speaker, tactile display, printing device, etc.). As used herein, a "portal" is hardware, firmware, and software to perform one or more of the computerization techniques described herein, without limitation. Includes any suitable combination of one or more devices configured with it. Examples of user devices that may implement the portal include, but are not limited to, personal computers, laptops, and mobile devices (smartphones, BlackBerry, PDAs, tablet computers, etc.). For example, the portal may be implemented via a web browser or mobile application installed on the user device. Only one server, one clinical portal 614, and one pharmacy portal 624 are shown in FIG. 6 to avoid complicating the drawings, and the system 600 has multiple servers as well as multiple clinicals. Can support portals and pharmacy portals.

In FIG. 6, patient 616 is examined by healthcare professional 618, who has access to the clinical portal 614. The patient may suffer from a disease with a known progression and consults with healthcare professional 618. Healthcare worker 618 makes measurements from patient 616 and records these measurements via the clinical portal 614. For example, healthcare professional 618 may take a blood sample of patient 616 or may measure the concentration of biomarkers in the blood sample. In general, healthcare professional 618 makes any suitable measurement of patient 616, including test results such as concentration measurements from the patient's blood, urine, saliva, or any other liquid sampled from the patient. May be good. The measurement may correspond to the observation of the patient 616 made by the healthcare professional 618, including any symptoms exhibited by the patient 616. For example, healthcare professional 618 may perform a patient examination and collect or measure physiological parameters, or determine the drug concentration in the patient. This involves identifying patient characteristics (physiological parameters and concentrations) that are reflected as patient factor covariates within a mathematical model that will be used to predict a patient's response to a drug treatment regimen. ..

For example, if the model is constructed to account for a typical patient response as a function of body weight and gender covariates, the patient's weight and gender characteristics will be identified. Any other property that has been shown to predict the response and is therefore reflected as a patient factor covariate within the mathematical model can be identified. As an example, such patient factor covariates are inflammation markers, albumin measurements, drug clearance indicators, C-reactive protein (CRP) measurements, antibody measurements, hematocrit levels, biomarkers of drug activity. , Weight, body size, gender, race, stage, disease status, previous therapy, previous laboratory results information, concomitant medications, complications, Mayo score, partial Mayo score, Harvey Bloodshow Indexes, blood pressure readings, psoriasis area, severity index (PASI) scores, and demographic information may be included. Based on the patient's measurement data, the healthcare professional 618 may assess the patient's disease status, administer to the patient 616, and identify the drug or class of drugs suitable for treating the patient 616. May be good. The clinical portal 614 then goes through the network 602 to the server 604, which uses the data received to select one or more suitable computational models from the model database 606, to the patient's measurements, (medical engagement). The patient's disease status (as determined by 618) and, optionally, the drug identifier may be transmitted.

A suitable computational model is determined to be capable of predicting a patient's response to administration of a drug or class of drugs. One or more selected computational models were used to determine a recommended set of planned dosages of the drug to be administered to the patient, and the recommendations were networked 602 for viewing by healthcare professionals 618. It will be returned to the clinical portal 614 via. Alternatively, healthcare professional 618 may not be able to assess the patient's disease status or identify the drug, and either or both of these steps may be performed by server 604. .. In this case, the server 604 receives the patient's measurement data and correlates the patient's measurement data with the data of other patients in the patient database 606A. Server 604 may then identify other patients with symptoms or data similar to patient 616 and determine disease status, drugs used, and outcomes for the other patient. Based on data from other patients, the server 604 identifies the drugs used that yielded the most common disease conditions and / or the most favorable outcomes, and these are to be considered by healthcare professionals 618. Results may be provided to clinical portal 614.

As shown in FIG. 6, the database 606 includes a set of four databases including a patient database 606A, a disease database 606B, a treatment planning database 606C, and a model database 606D. These databases store individual data about patients and their data, diseases, drugs, dosage schedules, and computational models. In particular, the patient database 606A stores measurements obtained by healthcare professional 618, including physiological parameter data and concentration data, or symptoms observed by healthcare professional 618. The disease database 606B stores data on various diseases and, often, possible symptoms presented by patients infected with the disease. The treatment plan database 606C stores data on possible treatment plans, including drug and dosage schedules for a set of patients. A set of patients may include, for example, a population with different characteristics such as weight, height, age, gender, and race.

The model database 606D stores data on a set of computational models that can be used to account for changes in pharmacokinetics (PK), pharmacodynamics (PD), or both PK and PD to the body. Any suitable mathematical model may be stored in the model database 606D, for example in the form of a compiled library module. In particular, a suitable mathematical model is a mathematical function (or group) that describes the relationship between the medication regimen and the observed patient exposure and / or observed patient response (collectively, "response") for a particular drug. A set of functions). Therefore, the mathematical model describes a response profile for the patient population. In general, developing a mathematical model involves developing a mathematical function or equation that best "fits" or describes the observed clinical data, defines a curve. Typical models also explain the expected impact of specific patient characteristics on response, as well as quantify the amount of unexplained variability that cannot be considered solely by patient characteristics. In such a model, patient characteristics are reflected as patient factor covariates in the mathematical model. Therefore, a mathematical model is typically a mathematical function that describes the underlying clinical data and the associated variability found in the patient population. These mathematical functions include terms that describe individual patient variability from "average" or typical patients, allowing the model to explain or predict various outcomes for a given dosage, and the model. Although not only mathematical functions but also statistical functions, models and functions are referred to herein as "mathematical" models and functions in a general and unrestricted manner.

In particular, the output of the model corresponds to a dosing regimen or schedule that achieves optimal target levels for the physiological parameters of patient 616. The model provides the optimal target level as a recommendation specifically designed for patient 616, verifying that the optimal target level is expected to produce an effective and therapeutic response in patient 616. There is. In the examples shown, the concentration data corresponds to the concentration of drug in the patient's blood. In some implementations, the drug may not be known, for example, the drug may be any drug in the drug category. Physiological parameter data may correspond to any number of measurements from the patient. For example, when the drug is infliximab, the drug concentration and other measurable units (which can be predicted by the model) such as C-reactive protein, endoscopic disease severity, and fecal calprotectin are measured. (And use the model to predict drug concentration) may be desirable. Each measurable material (eg, drug concentration, C-reactive protein, endoscopic disease severity, and fecal calprotectin) may be accompanied by one or more models such as PK or PD models. The interaction between the PK model and the PD model (modeled by higher clearance from the PK model, as detailed below) allows patients with more severe illness to take the drug faster. It can be of particular importance for drugs like infliximab that are wiped out. One goal of the drug infliximab may be to normalize C-reactive protein levels, reduce stool calprotectin levels, and achieve endoscopic remission.

In one embodiment, healthcare professional 618 may assess the likelihood that patient 616 will exhibit a therapeutic response to a particular drug and dosing regimen. In particular, this likelihood can be low if several dosing regimens of the same drug have been administered to the patient, but no measurable response from the patient has been detected. In this case, healthcare professional 618 may determine that the patient is unlikely to respond to further adjustments to the dosage, and other drugs may be considered. Confidence intervals may also be assessed for predictive model results and the body's predictive response to the presence of the drug. As data are collected from patient 616, the confidence intervals become narrower, presenting more reliable results and recommendations. The systems and methods described herein may identify personalized target levels (eg, target trough levels) or may provide personalized dosing recommendations based on personalized target levels.

In many cases, the healthcare worker 618 may be a member or employee of the healthcare center. The same patient 616 may meet with multiple members of the same medical center in different roles. In this case, the clinical portal 614 may be configured to operate on multiple user devices. The medical center may have its own record for a particular patient. In some implementations, the disclosure provides an interface between the computational model described herein and medical center records. For example, any healthcare professional 618, such as a doctor or nurse, may enter credentials (username, password, etc.) to log in to the system provided by the clinical portal 614, or via the user interface 612. You may be asked to scan the employee badge. Once logged in, each healthcare professional 618 may have a corresponding set of patient records that the healthcare professional may be able to access. In some implementations, patient 616 interacts with the clinical portal 614, which may have a patient-specific page or area for interaction with patient 616. For example, the clinical portal 614 may be configured to monitor the patient's treatment schedule and send appointments and reminders to the patient 616. Also, one or more devices (such as smart mobile devices or sensors) monitor the patient's ongoing physiological data and report the physiological data directly to the clinical portal 614 or to the server 604 via network 602. May be used for. Physiological data are then compared to expectations and deviations from expectations are flagged. Such intermittent monitoring of patient data may allow early detection of possible deviations from the patient's response to the drug and may indicate the need for amendment of dosing recommendations.

As described herein, measurements from patient 616 provided in the computational model are determined from healthcare professional 618, directly from a device that monitors patient 616, or a combination of both. May be. These measurements allow the treatment plan (provided by the model) to take into account specific patient data, as computational models predict disease and drug time progression, as well as their effects on the body. It may be used to update model parameters for refinement and correction. In some implementations, it is desirable to separate the patient's personal information from the patient's measurement data needed to launch the computational model. In particular, the patient's personal information may be protected health information (PHI) and access to the individual's PHI should be limited to authorized users. One way to protect a patient's PHI is to assign each patient to an anonymization code when the patient is enrolled using the server 604. The code may be manually typed by healthcare professional 618 via the clinical portal 614, or it may be typed using an automatic but secure process. Server 604 may only be able to identify each patient according to the anonymization code and may not have access to the patient's PHI. In particular, the clinical portal 614 and server 604 may exchange data about patient 616 without identifying patient 616 or revealing patient PHI. In some implementations, the clinical portal 614 is configured to communicate with the pharmacy portal 624 via network 602. In particular, after the dosing regimen has been selected to be administered to patient 616, healthcare professional 618 clinically injects the selected dosing regimen to transmit the selected dosing regimen to the pharmacy portal 624. It may be provided to portal 614. Upon receiving the dosing regimen, the pharmacy portal 624 may display the identifiers of the dosing regimen and the healthcare professional 618 via the user interface 622 that interacts with the pharmacist 628 to fulfill the order. ..

As shown in FIG. 6, the server 604 is a device (or set of devices) remote from the clinical portal 614. Depending on the computing power of the device accommodating the clinical portal 614, the clinical portal 614 may simply be an interface that primarily transfers data between the healthcare professional 618 and the server 604. Alternatively, the clinical portal 614 receives, but is not limited to, patient symptoms and measurement data, accesses one of the databases 606, launches one or more computational models, and patient specifics. Even if configured to perform any or all of the steps described by the server 604, including providing recommendations for dosage schedules based on symptoms and measurement data. good. FIG. 6 also depicts the patient database 606A, the disease database 606B, the treatment plan database 606C, and the model database 606D as being separate entities from the server 604, clinical portal 614, or pharmacy portal 624. Those skilled in the art will appreciate that any or all of Database 606 may be stored locally on any of the devices or portals described herein without departing from the scope of this disclosure. Will do.

FIG. 7A depicts an exemplary display screen (eg, user interface 612 of FIG. 6) displaying predicted concentration time profiles for four different dosing regimens. In particular, each dosing regimen has a corresponding dosing interval (5 weeks, 4 weeks) in which the dosage decreases as the dosing interval decreases (as indicated by the height of the second peak in each predicted concentration time profile). 3 weeks, and 2 weeks). In this case, the user is allowed to plot the IFX concentration against time, and to allow the computational model to be activated, identify the recommended dosing regimen, and maintain the IFX concentration above the critical trough value. You have selected.

In FIG. 7B, the user chooses to display the results from the plot shown in FIG. 7A in tabular form (eg, on the user interface 612 of FIG. 6). In particular, the exemplary display screen of FIG. 7B shows the last dosing date (January 2, 2015), various dosing intervals, dosing (predicted concentration time profile drops to or at clinical trough value). Trough day (corresponding to the first date after the day), suggested dosage (in mg), normalized suggested dosage (in mg / kg), number of vials used per dosing, and (in mg / kg). List target concentrations (in ng / ml units). As shown in FIG. 7B, the proposed set of dosing schedules is shown and the dosing schedules have different dosing intervals ranging from 2 to 8 weeks. Some of the dosing schedules with longer dosing intervals (6-8 weeks) are not recommended, but the four dosing schedules (with 2-5 week dosing intervals) increase as the dosing intervals increase. , Suggested with dosage. In particular, when interacting with the display screen of FIG. 7B, healthcare professional 618 may select a dosing regimen based on specific goals. For example, if it is desirable to administer the dosage to patient 616 infrequently, a longer dosing interval (eg, 5 weeks) may be selected. As an alternative, it is desirable to use as much of the vial as possible, as patients are often charged for the price of a complete vial, even when partial vials are used. In some cases. In this case, 4.9 vials are used for each dosing, leading to almost no drug disposal (eg, only 0.1 vials per dosage), so a 4-week dosing regimen is available. , May be selected. As an alternative, shorter dosing intervals (eg, 2 weeks) are selected if it is desirable to administer the dosage to patient 616 more frequently, or to charge patient 616 for only two vials at a time. You may.

Although various exemplary implementations have been described, it should be understood that the above description is merely exemplary and does not limit the scope of the invention. Although some embodiments are provided in the present disclosure, the disclosed systems, components, and manufacturing methods are embodied in many other embodiments without departing from the scope of the present disclosure. Please understand what you get.

The disclosed examples can be implemented in combination with or in combination with one or more other features described herein. Various devices, systems, and methods have been implemented under the present disclosure and may still fall within the scope of the invention. Also, the various features described or illustrated above may be combined or integrated in other systems, or some features may be omitted or not implemented.

Although various implementations of the present disclosure are shown and described herein, it will be apparent to those of skill in the art that such implementations are provided only as examples. Numerous variants, modifications, and substitutions will be recalled to those of skill in the art here without departing from the present disclosure. It is to be understood that various alternatives to the implementation of the present disclosure described herein may be employed in practicing the present disclosure.

All references cited herein are incorporated by reference in their entirety and are made part of this application.

Claims (75)

A method for generating a patient-specific medication regimen using a computerized medication regimen recommendation system with a processor and memory, said method.
(A) Receiving an input into the processor of the system, said input.
(I) Drug data indicating a plurality of drugs and associated routes of administration, wherein the drug among the plurality of drugs has similar pharmacokinetic (PK) behavior, similar pharmacodynamic (PD) behavior, and the like. With drug data, which is expected to present or both,
(Ii) Concentrations or response data indicating specific drug concentrations or response levels of the plurality of drugs in a sample obtained from the patient.
(Iii) Including targeted drug exposure or response level for said patient, and
(B) Selecting a mathematical model from a database stored in the memory, the database being accessible by the processor, storing a plurality of mathematical models, and the selected mathematical model. Representing a response to a plurality of drugs in the plurality of drugs by a plurality of patients, each response of the response represents a patient response to at least one drug in the plurality of drugs, and the mathematical model is a specific. It ’s not drug-specific,
(C) Multiple showing the patient's response to any drug among the plurality of drugs via the route of administration using the selected mathematical model and based on the concentration data and the route of administration. By inferring the predicted concentration-time profile of, each predicted concentration-time profile of the plurality of predicted concentration-time profiles corresponds to a dosing regimen in a plurality of dosing regimens, and each dosing regimen of the plurality of dosing regimens. Includes (i) at least one dosage and (ii) a recommended schedule for administering the at least one dosage to the patient.
(D) From the plurality of dosing regimens, selecting a first dosing regimen for the plurality of drugs inferred to achieve a therapeutic objective based on the target drug exposure or response level.
(E) A method comprising outputting the first dosing regimen for the plurality of drugs for the patient. The method of claim 1, wherein the drug data excludes information identifying the specific drug belonging to the plurality of drugs. The method further
Receiving additional drug data indicating the updated route of administration for the specific drug, and
To update the mathematical model based on the updated route of administration for the specific drug.
To calculate at least one updated dosing regimen to reach the therapeutic objective for the patient, based on the updated mathematical model.
The method of any of the claims, comprising outputting the at least one updated dosing regimen for the patient. The drug data further comprises one or more available dosage units corresponding to one or more drugs among the plurality of drugs, the dosage being a multiple of the available dosage unit. , The method according to any one of the above claims. The method according to any one of the above claims, wherein the model is a pharmacokinetic model. The method according to any one of the above claims, wherein the model is a pharmacodynamic model. The method further
An additional patient who exhibits a second response of the patient to the administration of a specific drug or another drug of the plurality of drugs according to the first dosing regimen or a modified version of the first dosing regimen. Receiving the data, the additional patient data is the concentration of one or more of the particular drug or another drug of the plurality of drugs in one or more samples obtained from the patient. Includes additional concentration data indicating the level, and
The math based on the patient's second response to administration of a specific drug or another drug of the plurality of drugs according to the first dosing regimen or a modified version of the first dosing regimen. Updating the model and
To calculate at least one updated dosing regimen to reach the therapeutic objective for the patient, based on the updated mathematical model.
The method of any of the claims, comprising outputting the at least one updated dosing regimen for the patient. The route of administration is at least one of subcutaneous, intravenous, oral, intramuscular, intrathecal, sublingual, intraoral, rectal, vaginal, intraocular, nasal, inhalation, spray, skin, and transdermal. The method according to any one of the above-mentioned claims. The plurality of drugs are one of a monoclonal antibody and an antibody construct, a cytokine, a drug used for enzyme replacement therapy, an aminoglycoside antibiotic, and a chemotherapeutic agent that causes leukocyte depletion. The method described in either. The method of any of the above claims, wherein each of the plurality of drugs shares a similar chemical structure. The method according to any one of the above claims, wherein each drug among the plurality of drugs shares a similar mechanism of action. The method of any of the claims, wherein the plurality of drugs are used to treat an inflammatory disease. The plurality of drugs are used to treat at least one of inflammatory bowel disease (IBD), rheumatoid arthritis, ankylosing spondylitis, psoriatic arthritis, psoriasis, asthma, and multiple sclerosis. , The method according to claim 12. The method of any of the claims, wherein the specific drug is infliximab. The method according to any one of claims 1-13, wherein the specific drug is adalimumab. The method of any of the preceding claims, wherein the received input comprises physiological data indicating one or more measurements of at least one physiological parameter of the patient. At least one physiological parameter of the patient is an inflammation marker, albumin measurement, drug clearance indicator, C-reactive protein (CRP) measurement, anti-drug antibody measurement, hematocrit level, biomarker of drug activity. , Weight, body size, gender, race, stage, disease status, previous therapy, previous clinical test result information, concomitant medications, complications, Mayo score, partial Mayo score, Harvey Bloodshow 16. The method of claim 16, comprising at least one of an index, a blood pressure reading, a psoriasis area, a severity index (PASI) score, a disease activity score (DAS), a sharp score, and demographic information. .. 17. The method of claim 17, wherein the method further comprises selecting from the set of mathematical models the mathematical model that best fits the received physiological data. The method according to any one of the above claims, wherein the model is a Bayesian model. For display purposes, (i) a patient-specific predicted concentration-time profile showing the patient's response to the plurality of drugs in response to the first dosing regimen, and (ii) the concentration data and the additional concentration data. The method of any of the aforementioned claims, further comprising generating at least a portion of the indications of. The method of any of the preceding claims, further comprising generating the target drug exposure or response level indication for display. The method of any of the above claims, wherein the model comprises a pharmacokinetic or pharmacodynamic component indicating the concentration or response time profile of the plurality of drugs. The method of any of the aforementioned claims, wherein the model comprises a pharmacodynamic component based on the synthesis and degradation rate of a pharmacodynamic marker indicating the individual response of the patient to the plurality of drugs. Further comprising receiving historical data indicating the patient's response to a second drug other than the specific drug, the computational model is to generate a prediction of the concentration-time profile of the plurality of drugs in the patient. The method of any of the claims, which takes into account the historical data. The drug data show that one or more available dosing intensities corresponding to one or more drugs in the plurality of drugs, and one or more drugs in the plurality of drugs are entirely of human origin. The method of any of the aforementioned claims, comprising at least one of one or more indicators indicating the presence or absence. The method of any of the claims, wherein the received input comprises patient data indicating a patient disease to be treated. A method for generating a patient-specific medication regimen using a computerized medication regimen recommendation system, said method.
(A) Receiving an input into the processor of the system, said input.
(I) Drug data indicating a plurality of drugs and routes of administration, wherein the drug among the plurality of drugs has a similar pharmacokinetic (PK) effect, a similar pharmacodynamic (PD) effect, or both. Expected to present with drug data,
(Ii) The initial drug concentration or response input data representing the initial comparison concentration or response data point,
(Iii) Including targeted drug exposure or response level for said patient, and
(B) Select a mathematical model that represents the response of a plurality of patients to a plurality of drugs in the plurality of drugs, wherein each response of the response is to at least one drug in the plurality of drugs. It shows the patient response and that the mathematical model is not specific to a particular drug.
(C) Multiple showing the patient's response to any drug among the plurality of drugs via the route of administration using the selected mathematical model and based on the patient data and the route of administration. By inferring the predicted concentration-time profile of, each predicted concentration-time profile of the plurality of predicted concentration-time profiles corresponds to a dosing regimen in a plurality of dosing regimens, and each dosing regimen of the plurality of dosing regimens. Includes (i) at least one dosage and (ii) a recommended schedule for administering the at least one dosage to the patient.
(D) From the plurality of dosing regimens, selecting a first dosing regimen for the plurality of drugs inferred to achieve a therapeutic objective based on the target drug exposure or response level.
(E) A method comprising outputting the first dosing regimen for the plurality of drugs for the patient. 27. The method of claim 27, wherein the initial comparative concentration data points indicate specific drug concentration levels of the plurality of drugs in a sample obtained from the patient. The drug data show that one or more available dosing intensities corresponding to one or more drugs in the plurality of drugs, and one or more drugs in the plurality of drugs are entirely of human origin. 27. The method of claim 27, comprising at least one of one or more indicators indicating the presence or absence. 27. The method of claim 27, wherein the initial comparative concentration data points are calculated based on the patient's physiological parameters. The patient's physiological parameters include inflammation markers, albumin measurements, drug clearance indicators, C-reactive protein (CRP) measurements, anti-drug antibody measurements, hematocrit levels, drug activity biomarkers, body weight, and more. Body size, gender, race, stage, disease status, previous therapy, previous clinical test result information, concomitant medications, complications, Mayo score, partial Mayo score, Harvey Bloodshaw index, blood pressure 30. The method of claim 30, comprising at least one of a reading, psoriasis area, severity index (PASI) score, disease activity score (DAS), sharp score, and demographic information. A system comprising a controller configured to implement the method of any of claims 1-31. Equipped with an interface connection to an electronic medical record database, the interface connection reads one or more indicators of patient information for a patient through the interface connection to determine the appropriate drug to administer to the patient. A system according to any of the systems according to claim, or the method according to any of the method claims, which is operational by software or firmware configured to use that information. A system for generating a patient-specific medication regimen, said system.
With memory
At least one processor, said at least one processor
(A) Receiving an input into the processor of the system, said input.
(I) Drug data indicating a plurality of drugs and associated routes of administration, wherein the drug among the plurality of drugs has similar pharmacokinetic (PK) behavior, similar pharmacodynamic (PD) behavior, and the like. With drug data, which is expected to present or both,
(Ii) Concentrations or response data indicating specific drug concentrations or response levels of the plurality of drugs in a sample obtained from the patient.
(Iii) Including targeted drug exposure or response level for said patient, and
(B) Selecting a mathematical model from a database stored in the memory, the database being accessible by the one or more processors, storing and selecting a plurality of mathematical models. The mathematical model represents a response by a plurality of patients to a plurality of drugs in the plurality of drugs, and each response of the response represents a patient response to at least one drug in the plurality of drugs, the mathematical model. Is not specific to a particular drug,
(C) Multiple showing the patient's response to any drug among the plurality of drugs via the route of administration using the selected mathematical model and based on the concentration data and the route of administration. By inferring the predicted concentration-time profile of, each predicted concentration-time profile of the plurality of predicted concentration-time profiles corresponds to a dosing regimen within a plurality of dosing regimens, and each dosing regimen of the plurality of dosing regimens. , (I) include at least one dosage and (ii) a recommended schedule for administering the at least one dosage to the patient.
(D) From the plurality of dosing regimens, selecting a first dosing regimen for the plurality of drugs inferred to achieve a therapeutic objective based on the target drug exposure or response level.
(E) A system comprising, for the patient, at least one processor configured to output the first dosing regimen for the plurality of drugs. 34. The system of claim 34, wherein the drug data excludes information identifying the particular drug belonging to the plurality of drugs. The at least one processor further
Receiving additional drug data indicating the updated route of administration for the specific drug, and
To update the mathematical model based on the updated route of administration for the specific drug.
To calculate at least one updated dosing regimen to reach the therapeutic objective for the patient, based on the updated mathematical model.
The system of any of claims 34 and 35 configured to output the at least one updated dosing regimen for the patient. The drug data further comprises one or more available dosage units corresponding to one or more drugs among the plurality of drugs, the dosage being a multiple of the available dosage unit. , A system according to any one of claims 34-36. The system according to any one of claims 34-37, wherein the model is a pharmacokinetic model. The system according to any one of claims 34-38, wherein the model is a pharmacodynamic model. The at least one processor further
An additional patient who exhibits a second response of the patient to the administration of a specific drug or another drug of the plurality of drugs according to the first dosing regimen or a modified version of the first dosing regimen. Receiving the data, the additional patient data is the concentration of one or more of the particular drug or another drug of the plurality of drugs in one or more samples obtained from the patient. Includes additional concentration data indicating the level, and
The math based on the patient's second response to administration of a specific drug or another drug of the plurality of drugs according to the first dosing regimen or a modified version of the first dosing regimen. Updating the model and
To calculate at least one updated dosing regimen to reach the therapeutic objective for the patient, based on the updated mathematical model.
30. The system of any of claims 34-39 configured to output the at least one updated dosing regimen for the patient. The route of administration is at least one of subcutaneous, intravenous, oral, intramuscular, intrathecal, sublingual, intraoral, rectal, vaginal, intraocular, nasal, inhalation, spray, skin, and transdermal. The system according to any one of claims 34-40. The plurality of drugs are one of monoclonal antibodies and antibody constructs, cytokines, drugs used for enzyme replacement therapy, aminoglycoside antibiotics, and chemotherapeutic agents that cause leukocyte depletion, claim 34-. The system according to any of 41. The system according to any one of claims 34-42, wherein each drug among the plurality of drugs shares a similar chemical structure. The system according to any one of claims 34-43, wherein each drug among the plurality of drugs shares a similar mechanism of action. The system according to any one of claims 34-44, wherein the plurality of drugs are used to treat an inflammatory disease. The plurality of drugs are used to treat at least one of inflammatory bowel disease (IBD), rheumatoid arthritis, ankylosing spondylitis, psoriatic arthritis, psoriasis, asthma, and multiple sclerosis. , The system according to claim 45. The system according to any one of claims 34-46, wherein the specific drug is infliximab. The system according to any one of claims 34-46, wherein the specific drug is adalimumab. The system of any of claims 34-48, wherein the received input comprises physiological data indicating one or more measurements of at least one physiological parameter of the patient. At least one physiological parameter of the patient is an inflammation marker, albumin measurement, drug clearance indicator, C-reactive protein (CRP) measurement, anti-drug antibody measurement, hematocrit level, biomarker of drug activity. , Weight, body size, gender, race, stage, disease status, previous therapy, previous clinical test result information, concomitant medications, complications, Mayo score, partial Mayo score, Harvey Bloodshow 49. The system of claim 49, comprising at least one of an index, a blood pressure reading, a psoriasis area, a severity index (PASI) score, a disease activity score (DAS), a sharp score, and demographic information. .. The system of claim 50, wherein the method further comprises selecting from the set of mathematical models the mathematical model that best fits the received physiological data. The system according to any one of claims 34-51, wherein the model is a Bayesian model. For display, the at least one processor further comprises (i) a patient-specific predicted concentration-time profile indicating the patient's response to the plurality of drugs in response to the first dosing regimen, and (ii) the concentration data. And the system of any of claims 34-52 configured to generate at least a portion of the additional concentration data. The system of any of claims 34-53, wherein the at least one processor is further configured to generate the target drug exposure or response level indication for display. The system according to any one of claims 34-54, wherein the model comprises a pharmacokinetic or pharmacodynamic component indicating the concentration time profile of the plurality of drugs. The system according to any one of claims 34-55, wherein the model comprises a pharmacodynamic component based on the synthesis and degradation rate of a pharmacodynamic marker indicating the individual response of the patient to the plurality of drugs. The at least one processor is further configured to receive historical data indicating the patient's response to a second drug other than the specific drug, and the computational model is such that the concentration time of the plurality of drugs in the patient. The system of any of claims 34-56, which takes into account the historical data to generate profile predictions. The drug data show that one or more available dosing intensities corresponding to one or more drugs in the plurality of drugs, and one or more drugs in the plurality of drugs are entirely of human origin. The system of any of claims 34-57, comprising at least one of one or more indicators indicating the presence or absence. The system of any of claims 34-58, wherein the received input comprises patient data indicating a patient disease to be treated. The system of any of claims 34-59, further comprising a cloud-based computing system operated by the at least one processor. The cloud-based computing system comprises a network and at least one server, wherein the network is a group of said at least one processor, said memory, said at least one server, and at least one user device. 60. The system of claim 60, configured to concatenate at least two selected from. Further comprising an interface connection to an electronic medical record database, the interface connection can be operated by software or firmware configured to read one or more indicators of patient information for the patient via the interface connection. The system according to any one of claims 34-61. A formulation for administration to a subject patient in a patient-specific dosing regimen determined by a computerized drug dosing regimen recommendation system with a processor and memory, said formulation.
Contains the active ingredient in the first dosage with the amount per unit,
The first dosage is determined by a model analysis, which is a model analysis.
(A) A step of receiving an input into the processor of the system, wherein the input is.
(I) Drug data indicating multiple drugs and associated routes of administration, wherein the drug among the plurality of drugs has similar pharmacokinetic (PK) behavior, similar pharmacodynamic (PD) behavior. , Or both, with drug data,
(Ii) Concentrations or response data indicating a first response corresponding to the initial dosage of the specific drug of the plurality of drugs in the sample obtained from the patient.
(Iii) Steps, including target drug exposure or response level for said patient, and
(B) A step of selecting a mathematical model from a database stored in the memory, the database being accessible by the processor, storing a plurality of mathematical models, and the selected mathematical model. Representing a response to a plurality of drugs in the plurality of drugs by a plurality of patients, each response of the response represents a patient response to at least one drug in the plurality of drugs, and the mathematical model is a specific. Steps that are not drug-specific,
(C) Multiple showing the patient's response to any drug among the plurality of drugs via the route of administration using the selected mathematical model and based on the concentration data and the route of administration. In the step of estimating the predicted concentration-time profile of, each predicted concentration-time profile of the plurality of predicted concentration-time profiles corresponds to the dosing regimen in the plurality of dosing regimens, and each dosing regimen of the plurality of dosing regimens. A step comprising (i) at least one dosage and (ii) a recommended schedule for administering the at least one dosage to the patient.
(D) A step of selecting from the plurality of medication regimens a first dosage regimen for the plurality of drugs inferred to achieve a therapeutic objective based on the target drug exposure or response level. The first dosing regimen comprises a step and a formulation comprising said first dosage. 13. The formulation according to claim 63, wherein the selected model is updated based on said concentration or response data prior to the step of inferring. The formulation contains the active ingredient in a second dosage and the model analysis further
An additional patient who exhibits a second response of the patient to the administration of a specific drug or another drug of the plurality of drugs according to the first dosing regimen or a modified version of the first dosing regimen. In the step of receiving data, the additional patient data is one or more of the specific drug or another drug of the plurality of drugs in one or more samples obtained from the patient. Steps and steps, including additional concentration data indicating the concentration level,
The math based on the patient's second response to administration of a specific drug or another drug of the plurality of drugs according to the first dosing regimen or a modified version of the first dosing regimen. Steps to update the model and
Based on the updated mathematical model, the step of calculating the updated dosing regimen to reach the therapeutic objective for the patient, wherein the updated dosing regimen is the second dosage. The pharmaceutical product according to any one of claims 63 and 64, comprising, and including steps. The pharmaceutical product according to any one of claims 63-65, wherein the renewal comprises a Bayesian renewal. The initial dosage is any of claims 63-66, which is the amount per unit of the particular drug administered to the patient in the initial dosing regimen prior to administration of the first dosing regimen. Dosing. The pharmaceutical product according to any one of claims 63-67, wherein the concentration or response data is obtained in the administration of the initial dosage. The pharmaceutical product according to any one of claims 63-68, wherein the received input comprises physiological data indicating one or more measurements of at least one physiological parameter of the patient. At least one physiological parameter of the patient is an inflammation marker, albumin measurement, drug clearance indicator, C-reactive protein (CRP) measurement, anti-drug antibody measurement, hematocrit level, biomarker of drug activity. , Weight, body size, gender, race, stage, disease status, previous therapy, previous clinical test result information, concomitant medications, complications, Mayo score, partial Mayo score, Harvey Bloodshow 29. The formulation according to claim 69, comprising at least one of an index, a blood pressure reading, a psoriasis area, a severity index (PASI) score, a disease activity score (DAS), a sharp score, and demographic information. medicine. The formulation according to any of claims 69 and 70, wherein the model analysis further comprises selecting from the set of mathematical models the mathematical model that best fits the received physiological data. The pharmaceutical product according to any one of claims 63-71, wherein the model is a Bayesian model. The formulation according to any one of claims 63-72, wherein the specific drug is infliximab. The pharmaceutical product according to any one of claims 63-73, wherein the model analysis comprises a Bayesian analysis. The pharmaceutical product according to any one of claims 63-74, wherein the model analysis is performed by a cloud-based computing system.

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